id	sid	tid	token	lemma	pos
ajst-4909	1	1	academic	academic	ADJ
ajst-4909	1	2	journal	journal	NOUN
ajst-4909	1	3	of	of	ADP
ajst-4909	1	4	science	science	NOUN
ajst-4909	1	5	and	and	CCONJ
ajst-4909	1	6	technology	technology	NOUN
ajst-4909	1	7	issn	issn	NOUN
ajst-4909	1	8	:	:	PUNCT
ajst-4909	1	9	2771	2771	NUM
ajst-4909	1	10	-	-	SYM
ajst-4909	1	11	3032	3032	NUM
ajst-4909	1	12	|	|	NOUN
ajst-4909	1	13	vol	vol	NOUN
ajst-4909	1	14	.	.	PROPN
ajst-4909	2	1	4	4	NUM
ajst-4909	2	2	,	,	PUNCT
ajst-4909	2	3	no	no	INTJ
ajst-4909	2	4	.	.	NOUN
ajst-4909	2	5	3	3	NUM
ajst-4909	2	6	,	,	PUNCT
ajst-4909	2	7	2022	2022	NUM
ajst-4909	2	8	116	116	NUM
ajst-4909	2	9	decomposition	decomposition	NOUN
ajst-4909	2	10	and	and	CCONJ
ajst-4909	2	11	classification	classification	NOUN
ajst-4909	2	12	of	of	ADP
ajst-4909	2	13	carbon	carbon	NOUN
ajst-4909	2	14	star	star	PROPN
ajst-4909	2	15	spectra	spectra	PROPN
ajst-4909	2	16	yuling	yuling	PROPN
ajst-4909	2	17	zhang	zhang	PROPN
ajst-4909	2	18	,	,	PUNCT
ajst-4909	2	19	yadong	yadong	PROPN
ajst-4909	2	20	wu	wu	PROPN
ajst-4909	2	21	*	*	PROPN
ajst-4909	2	22	sichuan	sichuan	PROPN
ajst-4909	2	23	university	university	PROPN
ajst-4909	2	24	of	of	ADP
ajst-4909	2	25	science	science	NOUN
ajst-4909	2	26	and	and	CCONJ
ajst-4909	2	27	engineering	engineering	NOUN
ajst-4909	2	28	,	,	PUNCT
ajst-4909	2	29	yibin	yibin	PROPN
ajst-4909	2	30	,	,	PUNCT
ajst-4909	2	31	china	china	PROPN
ajst-4909	2	32	abstract	abstract	PROPN
ajst-4909	2	33	:	:	PUNCT
ajst-4909	2	34	automatic	automatic	ADJ
ajst-4909	2	35	classification	classification	NOUN
ajst-4909	2	36	of	of	ADP
ajst-4909	2	37	stellar	stellar	ADJ
ajst-4909	2	38	spectra	spectra	NOUN
ajst-4909	2	39	is	be	AUX
ajst-4909	2	40	an	an	DET
ajst-4909	2	41	important	important	ADJ
ajst-4909	2	42	research	research	NOUN
ajst-4909	2	43	component	component	NOUN
ajst-4909	2	44	of	of	ADP
ajst-4909	2	45	astronomical	astronomical	ADJ
ajst-4909	2	46	data	datum	NOUN
ajst-4909	2	47	processing	processing	NOUN
ajst-4909	2	48	and	and	CCONJ
ajst-4909	2	49	is	be	AUX
ajst-4909	2	50	the	the	DET
ajst-4909	2	51	basis	basis	NOUN
ajst-4909	2	52	for	for	ADP
ajst-4909	2	53	studying	study	VERB
ajst-4909	2	54	stellar	stellar	ADJ
ajst-4909	2	55	evolution	evolution	NOUN
ajst-4909	2	56	and	and	CCONJ
ajst-4909	2	57	parameter	parameter	NOUN
ajst-4909	2	58	measurements	measurement	NOUN
ajst-4909	2	59	.	.	PUNCT
ajst-4909	3	1	as	as	ADP
ajst-4909	3	2	a	a	DET
ajst-4909	3	3	rare	rare	ADJ
ajst-4909	3	4	kind	kind	NOUN
ajst-4909	3	5	of	of	ADP
ajst-4909	3	6	stellar	stellar	ADJ
ajst-4909	3	7	spectra	spectra	NOUN
ajst-4909	3	8	,	,	PUNCT
ajst-4909	3	9	carbon	carbon	NOUN
ajst-4909	3	10	star	star	NOUN
ajst-4909	3	11	spectra	spectra	PROPN
ajst-4909	3	12	put	put	VERB
ajst-4909	3	13	forward	forward	ADV
ajst-4909	3	14	more	more	ADV
ajst-4909	3	15	efficient	efficient	ADJ
ajst-4909	3	16	and	and	CCONJ
ajst-4909	3	17	accurate	accurate	ADJ
ajst-4909	3	18	requirements	requirement	NOUN
ajst-4909	3	19	for	for	ADP
ajst-4909	3	20	classification	classification	NOUN
ajst-4909	3	21	methods	method	NOUN
ajst-4909	3	22	.	.	PUNCT
ajst-4909	4	1	the	the	DET
ajst-4909	4	2	traditional	traditional	ADJ
ajst-4909	4	3	manual	manual	ADJ
ajst-4909	4	4	classification	classification	NOUN
ajst-4909	4	5	methods	method	NOUN
ajst-4909	4	6	have	have	VERB
ajst-4909	4	7	the	the	DET
ajst-4909	4	8	disadvantages	disadvantage	NOUN
ajst-4909	4	9	of	of	ADP
ajst-4909	4	10	slow	slow	ADJ
ajst-4909	4	11	speed	speed	NOUN
ajst-4909	4	12	and	and	CCONJ
ajst-4909	4	13	low	low	ADJ
ajst-4909	4	14	accuracy	accuracy	NOUN
ajst-4909	4	15	,	,	PUNCT
ajst-4909	4	16	which	which	PRON
ajst-4909	4	17	can	can	AUX
ajst-4909	4	18	no	no	ADV
ajst-4909	4	19	longer	long	ADV
ajst-4909	4	20	meet	meet	VERB
ajst-4909	4	21	the	the	DET
ajst-4909	4	22	practical	practical	ADJ
ajst-4909	4	23	needs	need	NOUN
ajst-4909	4	24	of	of	ADP
ajst-4909	4	25	automatic	automatic	ADJ
ajst-4909	4	26	classification	classification	NOUN
ajst-4909	4	27	of	of	ADP
ajst-4909	4	28	massive	massive	ADJ
ajst-4909	4	29	stellar	stellar	ADJ
ajst-4909	4	30	spectra	spectra	NOUN
ajst-4909	4	31	,	,	PUNCT
ajst-4909	4	32	especially	especially	ADV
ajst-4909	4	33	low	low	ADJ
ajst-4909	4	34	signal	signal	NOUN
ajst-4909	4	35	-	-	PUNCT
ajst-4909	4	36	to	to	ADP
ajst-4909	4	37	-	-	PUNCT
ajst-4909	4	38	noise	noise	NOUN
ajst-4909	4	39	ratio	ratio	NOUN
ajst-4909	4	40	stellar	stellar	ADJ
ajst-4909	4	41	spectra	spectra	NOUN
ajst-4909	4	42	,	,	PUNCT
ajst-4909	4	43	and	and	CCONJ
ajst-4909	4	44	machine	machine	NOUN
ajst-4909	4	45	learning	learning	NOUN
ajst-4909	4	46	algorithms	algorithm	NOUN
ajst-4909	4	47	have	have	AUX
ajst-4909	4	48	been	be	AUX
ajst-4909	4	49	widely	widely	ADV
ajst-4909	4	50	applied	apply	VERB
ajst-4909	4	51	to	to	ADP
ajst-4909	4	52	stellar	stellar	ADJ
ajst-4909	4	53	spectral	spectral	ADJ
ajst-4909	4	54	classification	classification	NOUN
ajst-4909	4	55	.	.	PUNCT
ajst-4909	5	1	a	a	DET
ajst-4909	5	2	distinctive	distinctive	ADJ
ajst-4909	5	3	feature	feature	NOUN
ajst-4909	5	4	of	of	ADP
ajst-4909	5	5	stellar	stellar	ADJ
ajst-4909	5	6	spectra	spectra	NOUN
ajst-4909	5	7	is	be	AUX
ajst-4909	5	8	high	high	ADJ
ajst-4909	5	9	data	datum	NOUN
ajst-4909	5	10	dimensionality	dimensionality	NOUN
ajst-4909	5	11	,	,	PUNCT
ajst-4909	5	12	and	and	CCONJ
ajst-4909	5	13	dimensionality	dimensionality	NOUN
ajst-4909	5	14	reduction	reduction	NOUN
ajst-4909	5	15	can	can	AUX
ajst-4909	5	16	not	not	PART
ajst-4909	5	17	only	only	ADV
ajst-4909	5	18	realize	realize	VERB
ajst-4909	5	19	feature	feature	NOUN
ajst-4909	5	20	extraction	extraction	NOUN
ajst-4909	5	21	,	,	PUNCT
ajst-4909	5	22	but	but	CCONJ
ajst-4909	5	23	also	also	ADV
ajst-4909	5	24	reduce	reduce	VERB
ajst-4909	5	25	the	the	DET
ajst-4909	5	26	computational	computational	ADJ
ajst-4909	5	27	effort	effort	NOUN
ajst-4909	5	28	,	,	PUNCT
ajst-4909	5	29	which	which	PRON
ajst-4909	5	30	is	be	AUX
ajst-4909	5	31	the	the	DET
ajst-4909	5	32	first	first	ADJ
ajst-4909	5	33	task	task	NOUN
ajst-4909	5	34	of	of	ADP
ajst-4909	5	35	spectral	spectral	ADJ
ajst-4909	5	36	classification	classification	NOUN
ajst-4909	5	37	.	.	PUNCT
ajst-4909	6	1	traditional	traditional	ADJ
ajst-4909	6	2	linear	linear	ADJ
ajst-4909	6	3	dimensionality	dimensionality	NOUN
ajst-4909	6	4	reduction	reduction	NOUN
ajst-4909	6	5	methods	method	NOUN
ajst-4909	6	6	such	such	ADJ
ajst-4909	6	7	as	as	ADP
ajst-4909	6	8	principal	principal	ADJ
ajst-4909	6	9	component	component	NOUN
ajst-4909	6	10	analysis	analysis	NOUN
ajst-4909	6	11	reduce	reduce	VERB
ajst-4909	6	12	the	the	DET
ajst-4909	6	13	spectra	spectra	NOUN
ajst-4909	6	14	only	only	ADV
ajst-4909	6	15	based	base	VERB
ajst-4909	6	16	on	on	ADP
ajst-4909	6	17	the	the	DET
ajst-4909	6	18	variance	variance	NOUN
ajst-4909	6	19	,	,	PUNCT
ajst-4909	6	20	and	and	CCONJ
ajst-4909	6	21	different	different	ADJ
ajst-4909	6	22	types	type	NOUN
ajst-4909	6	23	of	of	ADP
ajst-4909	6	24	spectra	spectra	NOUN
ajst-4909	6	25	will	will	AUX
ajst-4909	6	26	appear	appear	VERB
ajst-4909	6	27	crossover	crossover	ADP
ajst-4909	6	28	after	after	ADP
ajst-4909	6	29	projection	projection	NOUN
ajst-4909	6	30	into	into	ADP
ajst-4909	6	31	the	the	DET
ajst-4909	6	32	low	low	ADJ
ajst-4909	6	33	-	-	PUNCT
ajst-4909	6	34	dimensional	dimensional	ADJ
ajst-4909	6	35	feature	feature	NOUN
ajst-4909	6	36	space	space	NOUN
ajst-4909	6	37	,	,	PUNCT
ajst-4909	6	38	while	while	SCONJ
ajst-4909	6	39	streamwise	streamwise	NOUN
ajst-4909	6	40	learning	learning	NOUN
ajst-4909	6	41	can	can	AUX
ajst-4909	6	42	produce	produce	VERB
ajst-4909	6	43	excellent	excellent	ADJ
ajst-4909	6	44	classification	classification	NOUN
ajst-4909	6	45	boundaries	boundary	NOUN
ajst-4909	6	46	,	,	PUNCT
ajst-4909	6	47	which	which	PRON
ajst-4909	6	48	will	will	AUX
ajst-4909	6	49	avoid	avoid	VERB
ajst-4909	6	50	overlap	overlap	NOUN
ajst-4909	6	51	and	and	CCONJ
ajst-4909	6	52	facilitate	facilitate	VERB
ajst-4909	6	53	subsequent	subsequent	ADJ
ajst-4909	6	54	classification	classification	NOUN
ajst-4909	6	55	.	.	PUNCT
ajst-4909	7	1	in	in	ADP
ajst-4909	7	2	view	view	NOUN
ajst-4909	7	3	of	of	ADP
ajst-4909	7	4	the	the	DET
ajst-4909	7	5	high	high	ADJ
ajst-4909	7	6	dimensionality	dimensionality	NOUN
ajst-4909	7	7	of	of	ADP
ajst-4909	7	8	spectral	spectral	ADJ
ajst-4909	7	9	data	datum	NOUN
ajst-4909	7	10	,	,	PUNCT
ajst-4909	7	11	we	we	PRON
ajst-4909	7	12	investigate	investigate	VERB
ajst-4909	7	13	the	the	DET
ajst-4909	7	14	distribution	distribution	NOUN
ajst-4909	7	15	of	of	ADP
ajst-4909	7	16	spectral	spectral	ADJ
ajst-4909	7	17	data	datum	NOUN
ajst-4909	7	18	in	in	ADP
ajst-4909	7	19	highdimensional	highdimensional	ADJ
ajst-4909	7	20	space	space	NOUN
ajst-4909	7	21	and	and	CCONJ
ajst-4909	7	22	the	the	DET
ajst-4909	7	23	principle	principle	NOUN
ajst-4909	7	24	of	of	ADP
ajst-4909	7	25	dimensionality	dimensionality	NOUN
ajst-4909	7	26	reduction	reduction	NOUN
ajst-4909	7	27	of	of	ADP
ajst-4909	7	28	high	high	ADJ
ajst-4909	7	29	-	-	PUNCT
ajst-4909	7	30	dimensional	dimensional	ADJ
ajst-4909	7	31	linear	linear	ADJ
ajst-4909	7	32	data	datum	NOUN
ajst-4909	7	33	by	by	ADP
ajst-4909	7	34	stream	stream	NOUN
ajst-4909	7	35	shape	shape	NOUN
ajst-4909	7	36	learning	learning	NOUN
ajst-4909	7	37	,	,	PUNCT
ajst-4909	7	38	compare	compare	VERB
ajst-4909	7	39	the	the	DET
ajst-4909	7	40	effect	effect	NOUN
ajst-4909	7	41	of	of	ADP
ajst-4909	7	42	two	two	NUM
ajst-4909	7	43	-	-	PUNCT
ajst-4909	7	44	dimensionality	dimensionality	NOUN
ajst-4909	7	45	reduction	reduction	NOUN
ajst-4909	7	46	methods	method	NOUN
ajst-4909	7	47	,	,	PUNCT
ajst-4909	7	48	t	t	PROPN
ajst-4909	7	49	sne	sne	PROPN
ajst-4909	7	50	and	and	CCONJ
ajst-4909	7	51	principal	principal	ADJ
ajst-4909	7	52	component	component	NOUN
ajst-4909	7	53	analysis	analysis	NOUN
ajst-4909	7	54	,	,	PUNCT
ajst-4909	7	55	on	on	ADP
ajst-4909	7	56	spectral	spectral	ADJ
ajst-4909	7	57	data	datum	NOUN
ajst-4909	7	58	,	,	PUNCT
ajst-4909	7	59	and	and	CCONJ
ajst-4909	7	60	finally	finally	ADV
ajst-4909	7	61	analyze	analyze	VERB
ajst-4909	7	62	the	the	DET
ajst-4909	7	63	experimental	experimental	ADJ
ajst-4909	7	64	results	result	NOUN
ajst-4909	7	65	and	and	CCONJ
ajst-4909	7	66	compare	compare	VERB
ajst-4909	7	67	and	and	CCONJ
ajst-4909	7	68	validate	validate	VERB
ajst-4909	7	69	them	they	PRON
ajst-4909	7	70	using	use	VERB
ajst-4909	7	71	various	various	ADJ
ajst-4909	7	72	machine	machine	NOUN
ajst-4909	7	73	learning	learn	VERB
ajst-4909	7	74	classifiers	classifier	NOUN
ajst-4909	7	75	.	.	PUNCT
ajst-4909	8	1	the	the	DET
ajst-4909	8	2	algorithm	algorithm	NOUN
ajst-4909	8	3	is	be	AUX
ajst-4909	8	4	implemented	implement	VERB
ajst-4909	8	5	using	use	VERB
ajst-4909	8	6	python	python	NOUN
ajst-4909	8	7	language	language	NOUN
ajst-4909	8	8	and	and	CCONJ
ajst-4909	8	9	scikit	scikit	NOUN
ajst-4909	8	10	learn	learn	VERB
ajst-4909	8	11	third	third	ADJ
ajst-4909	8	12	-	-	PUNCT
ajst-4909	8	13	party	party	NOUN
ajst-4909	8	14	library	library	NOUN
ajst-4909	8	15	to	to	PART
ajst-4909	8	16	perform	perform	VERB
ajst-4909	8	17	experiments	experiment	NOUN
ajst-4909	8	18	on	on	ADP
ajst-4909	8	19	1000	1000	NUM
ajst-4909	8	20	low	low	ADJ
ajst-4909	8	21	signal	signal	NOUN
ajst-4909	8	22	-	-	PUNCT
ajst-4909	8	23	to	to	ADP
ajst-4909	8	24	-	-	PUNCT
ajst-4909	8	25	noise	noise	NOUN
ajst-4909	8	26	carbon	carbon	NOUN
ajst-4909	8	27	star	star	NOUN
ajst-4909	8	28	spectra	spectra	NOUN
ajst-4909	8	29	from	from	ADP
ajst-4909	8	30	lamost	lamost	NOUN
ajst-4909	8	31	,	,	PUNCT
ajst-4909	8	32	and	and	CCONJ
ajst-4909	8	33	finally	finally	ADV
ajst-4909	8	34	achieve	achieve	VERB
ajst-4909	8	35	high	high	ADJ
ajst-4909	8	36	accuracy	accuracy	NOUN
ajst-4909	8	37	automatic	automatic	ADJ
ajst-4909	8	38	processing	processing	NOUN
ajst-4909	8	39	and	and	CCONJ
ajst-4909	8	40	classification	classification	NOUN
ajst-4909	8	41	of	of	ADP
ajst-4909	8	42	the	the	DET
ajst-4909	8	43	spectral	spectral	ADJ
ajst-4909	8	44	data	datum	NOUN
ajst-4909	8	45	.	.	PUNCT
ajst-4909	9	1	the	the	DET
ajst-4909	9	2	experimental	experimental	ADJ
ajst-4909	9	3	results	result	NOUN
ajst-4909	9	4	show	show	VERB
ajst-4909	9	5	that	that	SCONJ
ajst-4909	9	6	for	for	ADP
ajst-4909	9	7	the	the	DET
ajst-4909	9	8	dimensionality	dimensionality	NOUN
ajst-4909	9	9	reduction	reduction	NOUN
ajst-4909	9	10	processing	processing	NOUN
ajst-4909	9	11	of	of	ADP
ajst-4909	9	12	spectral	spectral	ADJ
ajst-4909	9	13	data	datum	NOUN
ajst-4909	9	14	,	,	PUNCT
ajst-4909	9	15	the	the	DET
ajst-4909	9	16	t	t	PROPN
ajst-4909	9	17	sne	sne	PROPN
ajst-4909	9	18	method	method	NOUN
ajst-4909	9	19	based	base	VERB
ajst-4909	9	20	on	on	ADP
ajst-4909	9	21	stream	stream	NOUN
ajst-4909	9	22	shape	shape	NOUN
ajst-4909	9	23	learning	learning	NOUN
ajst-4909	9	24	can	can	AUX
ajst-4909	9	25	recover	recover	VERB
ajst-4909	9	26	the	the	DET
ajst-4909	9	27	low	low	ADV
ajst-4909	9	28	-	-	PUNCT
ajst-4909	9	29	dimensional	dimensional	ADJ
ajst-4909	9	30	stream	stream	NOUN
ajst-4909	9	31	shape	shape	NOUN
ajst-4909	9	32	structure	structure	NOUN
ajst-4909	9	33	in	in	ADP
ajst-4909	9	34	the	the	DET
ajst-4909	9	35	high	high	ADV
ajst-4909	9	36	-	-	PUNCT
ajst-4909	9	37	dimensional	dimensional	ADJ
ajst-4909	9	38	spectral	spectral	ADJ
ajst-4909	9	39	data	datum	NOUN
ajst-4909	9	40	,	,	PUNCT
ajst-4909	9	41	and	and	CCONJ
ajst-4909	9	42	after	after	ADP
ajst-4909	9	43	feature	feature	NOUN
ajst-4909	9	44	extraction	extraction	NOUN
ajst-4909	9	45	,	,	PUNCT
ajst-4909	9	46	satisfactory	satisfactory	ADJ
ajst-4909	9	47	classification	classification	NOUN
ajst-4909	9	48	accuracy	accuracy	NOUN
ajst-4909	9	49	can	can	AUX
ajst-4909	9	50	be	be	AUX
ajst-4909	9	51	achieved	achieve	VERB
ajst-4909	9	52	on	on	ADP
ajst-4909	9	53	the	the	DET
ajst-4909	9	54	test	test	NOUN
ajst-4909	9	55	dataset	dataset	NOUN
ajst-4909	9	56	using	use	VERB
ajst-4909	9	57	a	a	DET
ajst-4909	9	58	machine	machine	NOUN
ajst-4909	9	59	learning	learn	VERB
ajst-4909	9	60	classifier	classifier	NOUN
ajst-4909	9	61	.	.	PUNCT
ajst-4909	10	1	keywords	keyword	NOUN
ajst-4909	10	2	:	:	PUNCT
ajst-4909	10	3	classification	classification	NOUN
ajst-4909	10	4	of	of	ADP
ajst-4909	10	5	stellar	stellar	ADJ
ajst-4909	10	6	spectra	spectra	NOUN
ajst-4909	10	7	,	,	PUNCT
ajst-4909	10	8	data	datum	NOUN
ajst-4909	10	9	reduction	reduction	NOUN
ajst-4909	10	10	,	,	PUNCT
ajst-4909	10	11	carbon	carbon	NOUN
ajst-4909	10	12	star	star	NOUN
ajst-4909	10	13	,	,	PUNCT
ajst-4909	10	14	manifold	manifold	ADJ
ajst-4909	10	15	learning	learning	NOUN
ajst-4909	10	16	.	.	PUNCT
ajst-4909	11	1	1	1	X
ajst-4909	11	2	.	.	X
ajst-4909	11	3	introduction	introduction	NOUN
ajst-4909	11	4	with	with	ADP
ajst-4909	11	5	the	the	DET
ajst-4909	11	6	expansion	expansion	NOUN
ajst-4909	11	7	of	of	ADP
ajst-4909	11	8	modern	modern	ADJ
ajst-4909	11	9	astronomical	astronomical	ADJ
ajst-4909	11	10	survey	survey	NOUN
ajst-4909	11	11	projects	project	NOUN
ajst-4909	11	12	generating	generate	VERB
ajst-4909	11	13	huge	huge	ADJ
ajst-4909	11	14	volumes	volume	NOUN
ajst-4909	11	15	of	of	ADP
ajst-4909	11	16	survey	survey	NOUN
ajst-4909	11	17	data	datum	NOUN
ajst-4909	11	18	,	,	PUNCT
ajst-4909	11	19	manual	manual	ADJ
ajst-4909	11	20	classification	classification	NOUN
ajst-4909	11	21	methods	method	NOUN
ajst-4909	11	22	through	through	ADP
ajst-4909	11	23	traditional	traditional	ADJ
ajst-4909	11	24	spectroscopy	spectroscopy	NOUN
ajst-4909	11	25	methods	method	NOUN
ajst-4909	11	26	can	can	AUX
ajst-4909	11	27	no	no	ADV
ajst-4909	11	28	longer	long	ADV
ajst-4909	11	29	meet	meet	VERB
ajst-4909	11	30	the	the	DET
ajst-4909	11	31	needs	need	NOUN
ajst-4909	11	32	of	of	ADP
ajst-4909	11	33	modern	modern	ADJ
ajst-4909	11	34	survey	survey	NOUN
ajst-4909	11	35	missions	mission	NOUN
ajst-4909	11	36	for	for	ADP
ajst-4909	11	37	high	high	ADJ
ajst-4909	11	38	efficiency	efficiency	NOUN
ajst-4909	11	39	,	,	PUNCT
ajst-4909	11	40	high	high	ADJ
ajst-4909	11	41	accuracy	accuracy	NOUN
ajst-4909	11	42	,	,	PUNCT
ajst-4909	11	43	and	and	CCONJ
ajst-4909	11	44	low	low	ADJ
ajst-4909	11	45	labor	labor	NOUN
ajst-4909	11	46	cost	cost	NOUN
ajst-4909	11	47	.	.	PUNCT
ajst-4909	12	1	machine	machine	NOUN
ajst-4909	12	2	learning	learn	VERB
ajst-4909	12	3	algorithms	algorithm	NOUN
ajst-4909	12	4	are	be	AUX
ajst-4909	12	5	now	now	ADV
ajst-4909	12	6	widely	widely	ADV
ajst-4909	12	7	used	use	VERB
ajst-4909	12	8	in	in	ADP
ajst-4909	12	9	astronomical	astronomical	ADJ
ajst-4909	12	10	spectral	spectral	ADJ
ajst-4909	12	11	classification	classification	NOUN
ajst-4909	12	12	and	and	CCONJ
ajst-4909	12	13	have	have	AUX
ajst-4909	12	14	achieved	achieve	VERB
ajst-4909	12	15	good	good	ADJ
ajst-4909	12	16	results	result	NOUN
ajst-4909	12	17	.	.	PUNCT
ajst-4909	13	1	navarro	navarro	PROPN
ajst-4909	14	1	[	[	X
ajst-4909	14	2	1	1	X
ajst-4909	14	3	]	]	PUNCT
ajst-4909	14	4	used	use	VERB
ajst-4909	14	5	artificial	artificial	ADJ
ajst-4909	14	6	neural	neural	ADJ
ajst-4909	14	7	networks	network	NOUN
ajst-4909	14	8	to	to	PART
ajst-4909	14	9	classify	classify	VERB
ajst-4909	14	10	spectral	spectral	ADJ
ajst-4909	14	11	data	datum	NOUN
ajst-4909	14	12	with	with	ADP
ajst-4909	14	13	different	different	ADJ
ajst-4909	14	14	signal	signal	NOUN
ajst-4909	14	15	-	-	PUNCT
ajst-4909	14	16	to	to	ADP
ajst-4909	14	17	-	-	PUNCT
ajst-4909	14	18	noise	noise	NOUN
ajst-4909	14	19	ratios	ratio	NOUN
ajst-4909	14	20	,	,	PUNCT
ajst-4909	14	21	and	and	CCONJ
ajst-4909	14	22	the	the	DET
ajst-4909	14	23	classification	classification	NOUN
ajst-4909	14	24	results	result	NOUN
ajst-4909	14	25	have	have	VERB
ajst-4909	14	26	high	high	ADJ
ajst-4909	14	27	confidence	confidence	NOUN
ajst-4909	14	28	for	for	ADP
ajst-4909	14	29	spectral	spectral	ADJ
ajst-4909	14	30	data	datum	NOUN
ajst-4909	14	31	with	with	ADP
ajst-4909	14	32	low	low	ADJ
ajst-4909	14	33	signal	signal	NOUN
ajst-4909	14	34	-	-	PUNCT
ajst-4909	14	35	to	to	ADP
ajst-4909	14	36	-	-	PUNCT
ajst-4909	14	37	noise	noise	NOUN
ajst-4909	14	38	ratios	ratio	NOUN
ajst-4909	14	39	as	as	ADV
ajst-4909	14	40	well	well	ADV
ajst-4909	14	41	;	;	PUNCT
ajst-4909	14	42	kheirdastan	kheirdastan	PROPN
ajst-4909	15	1	[	[	X
ajst-4909	15	2	2	2	NUM
ajst-4909	15	3	,	,	PUNCT
ajst-4909	15	4	3	3	NUM
ajst-4909	15	5	]	]	PUNCT
ajst-4909	15	6	used	use	VERB
ajst-4909	15	7	probabilistic	probabilistic	ADJ
ajst-4909	15	8	neural	neural	ADJ
ajst-4909	15	9	networks	network	NOUN
ajst-4909	15	10	as	as	ADP
ajst-4909	15	11	an	an	DET
ajst-4909	15	12	automatic	automatic	ADJ
ajst-4909	15	13	classification	classification	NOUN
ajst-4909	15	14	tool	tool	NOUN
ajst-4909	15	15	for	for	ADP
ajst-4909	15	16	massive	massive	ADJ
ajst-4909	15	17	stellar	stellar	ADJ
ajst-4909	15	18	spectra	spectra	NOUN
ajst-4909	15	19	and	and	CCONJ
ajst-4909	15	20	obtained	obtain	VERB
ajst-4909	15	21	accurate	accurate	ADJ
ajst-4909	15	22	spectral	spectral	ADJ
ajst-4909	15	23	-	-	PUNCT
ajst-4909	15	24	type	type	NOUN
ajst-4909	15	25	classification	classification	NOUN
ajst-4909	15	26	results	result	NOUN
ajst-4909	15	27	.	.	PUNCT
ajst-4909	16	1	in	in	ADP
ajst-4909	16	2	addition	addition	NOUN
ajst-4909	16	3	,	,	PUNCT
ajst-4909	16	4	the	the	DET
ajst-4909	16	5	classification	classification	NOUN
ajst-4909	16	6	results	result	NOUN
ajst-4909	16	7	of	of	ADP
ajst-4909	16	8	stellar	stellar	ADJ
ajst-4909	16	9	spectral	spectral	ADJ
ajst-4909	16	10	data	datum	NOUN
ajst-4909	16	11	using	use	VERB
ajst-4909	16	12	an	an	DET
ajst-4909	16	13	entropy	entropy	NOUN
ajst-4909	16	14	learning	learn	VERB
ajst-4909	16	15	machine	machine	NOUN
ajst-4909	16	16	are	be	AUX
ajst-4909	16	17	also	also	ADV
ajst-4909	16	18	more	more	ADV
ajst-4909	16	19	accurate	accurate	ADJ
ajst-4909	16	20	;	;	PUNCT
ajst-4909	16	21	chen	chen	PROPN
ajst-4909	17	1	[	[	X
ajst-4909	17	2	4	4	X
ajst-4909	17	3	]	]	PUNCT
ajst-4909	17	4	improved	improve	VERB
ajst-4909	17	5	the	the	DET
ajst-4909	17	6	efficiency	efficiency	NOUN
ajst-4909	17	7	of	of	ADP
ajst-4909	17	8	spectral	spectral	ADJ
ajst-4909	17	9	classification	classification	NOUN
ajst-4909	17	10	using	use	VERB
ajst-4909	17	11	a	a	DET
ajst-4909	17	12	restricted	restricted	ADJ
ajst-4909	17	13	boltzmann	boltzmann	NOUN
ajst-4909	17	14	machine	machine	NOUN
ajst-4909	17	15	.	.	PUNCT
ajst-4909	18	1	carbon	carbon	NOUN
ajst-4909	18	2	stars	star	NOUN
ajst-4909	18	3	are	be	AUX
ajst-4909	18	4	rare	rare	ADJ
ajst-4909	18	5	objects	object	NOUN
ajst-4909	18	6	,	,	PUNCT
ajst-4909	18	7	first	first	ADV
ajst-4909	18	8	discovered	discover	VERB
ajst-4909	18	9	and	and	CCONJ
ajst-4909	18	10	studied	study	VERB
ajst-4909	18	11	by	by	ADP
ajst-4909	18	12	the	the	DET
ajst-4909	18	13	italian	italian	ADJ
ajst-4909	18	14	astronomer	astronomer	NOUN
ajst-4909	18	15	secchi	secchi	PROPN
ajst-4909	19	1	[	[	X
ajst-4909	19	2	5	5	NUM
ajst-4909	19	3	]	]	PUNCT
ajst-4909	19	4	in	in	ADP
ajst-4909	19	5	1869	1869	NUM
ajst-4909	19	6	.	.	PUNCT
ajst-4909	20	1	compared	compare	VERB
ajst-4909	20	2	with	with	ADP
ajst-4909	20	3	ordinary	ordinary	ADJ
ajst-4909	20	4	stars	star	NOUN
ajst-4909	20	5	,	,	PUNCT
ajst-4909	20	6	carbon	carbon	NOUN
ajst-4909	20	7	stars	star	NOUN
ajst-4909	20	8	have	have	VERB
ajst-4909	20	9	unique	unique	ADJ
ajst-4909	20	10	physical	physical	ADJ
ajst-4909	20	11	properties	property	NOUN
ajst-4909	20	12	,	,	PUNCT
ajst-4909	20	13	such	such	ADJ
ajst-4909	20	14	as	as	ADP
ajst-4909	20	15	a	a	DET
ajst-4909	20	16	higher	high	ADJ
ajst-4909	20	17	content	content	NOUN
ajst-4909	20	18	of	of	ADP
ajst-4909	20	19	carbon	carbon	NOUN
ajst-4909	20	20	than	than	ADP
ajst-4909	20	21	oxygen	oxygen	NOUN
ajst-4909	20	22	in	in	ADP
ajst-4909	20	23	the	the	DET
ajst-4909	20	24	atmosphere	atmosphere	NOUN
ajst-4909	20	25	(	(	PUNCT
ajst-4909	20	26	c	c	X
ajst-4909	20	27	/	/	SYM
ajst-4909	20	28	o>1	o>1	PROPN
ajst-4909	20	29	)	)	PUNCT
ajst-4909	20	30	and	and	CCONJ
ajst-4909	20	31	a	a	DET
ajst-4909	20	32	spectrum	spectrum	NOUN
ajst-4909	20	33	characterized	characterize	VERB
ajst-4909	20	34	by	by	ADP
ajst-4909	20	35	strong	strong	ADJ
ajst-4909	20	36	carbon	carbon	NOUN
ajst-4909	20	37	molecular	molecular	ADJ
ajst-4909	20	38	bands	band	NOUN
ajst-4909	20	39	of	of	ADP
ajst-4909	20	40	ch	ch	NOUN
ajst-4909	20	41	,	,	PUNCT
ajst-4909	20	42	cn	cn	PROPN
ajst-4909	20	43	,	,	PUNCT
ajst-4909	20	44	and	and	CCONJ
ajst-4909	20	45	c2	c2	PROPN
ajst-4909	20	46	,	,	PUNCT
ajst-4909	20	47	making	make	VERB
ajst-4909	20	48	it	it	PRON
ajst-4909	20	49	of	of	ADP
ajst-4909	20	50	great	great	ADJ
ajst-4909	20	51	importance	importance	NOUN
ajst-4909	20	52	for	for	ADP
ajst-4909	20	53	the	the	DET
ajst-4909	20	54	study	study	NOUN
ajst-4909	20	55	of	of	ADP
ajst-4909	20	56	galactic	galactic	ADJ
ajst-4909	20	57	structure	structure	NOUN
ajst-4909	20	58	,	,	PUNCT
ajst-4909	20	59	near	near	ADP
ajst-4909	20	60	-	-	PUNCT
ajst-4909	20	61	field	field	NOUN
ajst-4909	20	62	cosmology	cosmology	NOUN
ajst-4909	20	63	,	,	PUNCT
ajst-4909	20	64	and	and	CCONJ
ajst-4909	20	65	the	the	DET
ajst-4909	20	66	measurement	measurement	NOUN
ajst-4909	20	67	of	of	ADP
ajst-4909	20	68	galactic	galactic	ADJ
ajst-4909	20	69	rotation	rotation	NOUN
ajst-4909	20	70	curves	curve	NOUN
ajst-4909	20	71	[	[	X
ajst-4909	20	72	6	6	NUM
ajst-4909	20	73	]	]	PUNCT
ajst-4909	20	74	.	.	PUNCT
ajst-4909	21	1	the	the	DET
ajst-4909	21	2	carbon	carbon	NOUN
ajst-4909	21	3	star	star	NOUN
ajst-4909	21	4	spectra	spectra	PROPN
ajst-4909	21	5	are	be	AUX
ajst-4909	21	6	classified	classify	VERB
ajst-4909	21	7	into	into	ADP
ajst-4909	21	8	five	five	NUM
ajst-4909	21	9	types	type	NOUN
ajst-4909	21	10	of	of	ADP
ajst-4909	21	11	spectroscopic	spectroscopic	ADJ
ajst-4909	21	12	spectra	spectra	NOUN
ajst-4909	21	13	,	,	PUNCT
ajst-4909	21	14	c	c	NOUN
ajst-4909	21	15	-	-	PUNCT
ajst-4909	21	16	h	h	NOUN
ajst-4909	21	17	,	,	PUNCT
ajst-4909	21	18	c	c	NOUN
ajst-4909	21	19	-	-	PUNCT
ajst-4909	21	20	n	n	CCONJ
ajst-4909	21	21	,	,	PUNCT
ajst-4909	21	22	c	c	PROPN
ajst-4909	21	23	-	-	PUNCT
ajst-4909	21	24	j	j	NOUN
ajst-4909	21	25	,	,	PUNCT
ajst-4909	21	26	c	c	NOUN
ajst-4909	21	27	-	-	PUNCT
ajst-4909	21	28	r	r	NOUN
ajst-4909	21	29	,	,	PUNCT
ajst-4909	21	30	and	and	CCONJ
ajst-4909	21	31	ba	ba	PROPN
ajst-4909	21	32	,	,	PUNCT
ajst-4909	21	33	according	accord	VERB
ajst-4909	21	34	to	to	ADP
ajst-4909	21	35	the	the	DET
ajst-4909	21	36	keenan	keenan	NOUN
ajst-4909	21	37	[	[	X
ajst-4909	21	38	7	7	NUM
ajst-4909	21	39	]	]	X
ajst-4909	21	40	modified	modify	VERB
ajst-4909	21	41	mk	mk	PROPN
ajst-4909	21	42	carbon	carbon	NOUN
ajst-4909	21	43	star	star	NOUN
ajst-4909	21	44	classification	classification	NOUN
ajst-4909	21	45	.	.	PUNCT
ajst-4909	22	1	different	different	ADJ
ajst-4909	22	2	types	type	NOUN
ajst-4909	22	3	of	of	ADP
ajst-4909	22	4	carbon	carbon	NOUN
ajst-4909	22	5	stars	star	NOUN
ajst-4909	22	6	have	have	VERB
ajst-4909	22	7	different	different	ADJ
ajst-4909	22	8	metal	metal	NOUN
ajst-4909	22	9	abundances	abundance	NOUN
ajst-4909	22	10	,	,	PUNCT
ajst-4909	22	11	different	different	ADJ
ajst-4909	22	12	galactic	galactic	ADJ
ajst-4909	22	13	distribution	distribution	NOUN
ajst-4909	22	14	locations	location	NOUN
ajst-4909	22	15	,	,	PUNCT
ajst-4909	22	16	different	different	ADJ
ajst-4909	22	17	brightnesses	brightness	NOUN
ajst-4909	22	18	,	,	PUNCT
ajst-4909	22	19	different	different	ADJ
ajst-4909	22	20	kinematic	kinematic	ADJ
ajst-4909	22	21	velocities	velocity	NOUN
ajst-4909	22	22	,	,	PUNCT
ajst-4909	22	23	and	and	CCONJ
ajst-4909	22	24	are	be	AUX
ajst-4909	22	25	at	at	ADP
ajst-4909	22	26	different	different	ADJ
ajst-4909	22	27	evolutionary	evolutionary	ADJ
ajst-4909	22	28	stages	stage	NOUN
ajst-4909	22	29	,	,	PUNCT
ajst-4909	22	30	so	so	CCONJ
ajst-4909	22	31	the	the	DET
ajst-4909	22	32	study	study	NOUN
ajst-4909	22	33	of	of	ADP
ajst-4909	22	34	carbon	carbon	NOUN
ajst-4909	22	35	star	star	NOUN
ajst-4909	22	36	classification	classification	NOUN
ajst-4909	22	37	plays	play	VERB
ajst-4909	22	38	a	a	DET
ajst-4909	22	39	crucial	crucial	ADJ
ajst-4909	22	40	role	role	NOUN
ajst-4909	22	41	for	for	SCONJ
ajst-4909	22	42	astronomers	astronomer	NOUN
ajst-4909	22	43	to	to	PART
ajst-4909	22	44	study	study	VERB
ajst-4909	22	45	carbon	carbon	NOUN
ajst-4909	22	46	stars	star	NOUN
ajst-4909	22	47	.	.	PUNCT
ajst-4909	23	1	lamost	lamost	NOUN
ajst-4909	23	2	(	(	PUNCT
ajst-4909	23	3	large	large	ADJ
ajst-4909	23	4	sky	sky	NOUN
ajst-4909	23	5	area	area	NOUN
ajst-4909	23	6	multi	multi	ADJ
ajst-4909	23	7	-	-	ADJ
ajst-4909	23	8	object	object	ADJ
ajst-4909	23	9	fiber	fiber	NOUN
ajst-4909	23	10	spectroscopic	spectroscopic	NOUN
ajst-4909	23	11	telescope	telescope	NOUN
ajst-4909	23	12	,	,	PUNCT
ajst-4909	23	13	lamost	lamost	NOUN
ajst-4909	23	14	)	)	PUNCT
ajst-4909	24	1	[	[	X
ajst-4909	24	2	8	8	NUM
ajst-4909	24	3	]	]	PUNCT
ajst-4909	24	4	,	,	PUNCT
ajst-4909	24	5	also	also	ADV
ajst-4909	24	6	known	know	VERB
ajst-4909	24	7	as	as	ADP
ajst-4909	24	8	guo	guo	PROPN
ajst-4909	24	9	shoujing	shoujing	NOUN
ajst-4909	24	10	telescope	telescope	NOUN
ajst-4909	24	11	,	,	PUNCT
ajst-4909	24	12	is	be	AUX
ajst-4909	24	13	china	china	PROPN
ajst-4909	24	14	's	's	PART
ajst-4909	24	15	independent	independent	ADJ
ajst-4909	24	16	innovation	innovation	NOUN
ajst-4909	24	17	,	,	PUNCT
ajst-4909	24	18	the	the	DET
ajst-4909	24	19	world	world	NOUN
ajst-4909	24	20	's	's	PART
ajst-4909	24	21	largest	large	ADJ
ajst-4909	24	22	aperture	aperture	NOUN
ajst-4909	24	23	of	of	ADP
ajst-4909	24	24	large	large	ADJ
ajst-4909	24	25	viewport	viewport	NOUN
ajst-4909	24	26	cum	cum	NOUN
ajst-4909	24	27	large	large	ADJ
ajst-4909	24	28	aperture	aperture	NOUN
ajst-4909	24	29	and	and	CCONJ
ajst-4909	24	30	spectral	spectral	ADJ
ajst-4909	24	31	acquisition	acquisition	NOUN
ajst-4909	24	32	rate	rate	NOUN
ajst-4909	24	33	of	of	ADP
ajst-4909	24	34	the	the	DET
ajst-4909	24	35	telescope	telescope	NOUN
ajst-4909	24	36	,	,	PUNCT
ajst-4909	24	37	a	a	DET
ajst-4909	24	38	4	4	NUM
ajst-4909	24	39	-	-	PUNCT
ajst-4909	24	40	meter	meter	NOUN
ajst-4909	24	41	reflecting	reflect	VERB
ajst-4909	24	42	schmidt	schmidt	NOUN
ajst-4909	24	43	telescope	telescope	NOUN
ajst-4909	24	44	,	,	PUNCT
ajst-4909	24	45	in	in	ADP
ajst-4909	24	46	20	20	NUM
ajst-4909	24	47	square	square	ADJ
ajst-4909	24	48	degrees	degree	NOUN
ajst-4909	24	49	of	of	ADP
ajst-4909	24	50	the	the	DET
ajst-4909	24	51	focal	focal	ADJ
ajst-4909	24	52	plane	plane	NOUN
ajst-4909	24	53	with	with	ADP
ajst-4909	24	54	4,000	4,000	NUM
ajst-4909	24	55	optical	optical	ADJ
ajst-4909	24	56	fibers	fiber	NOUN
ajst-4909	24	57	.	.	PUNCT
ajst-4909	25	1	in	in	ADP
ajst-4909	25	2	march	march	PROPN
ajst-4909	25	3	2020	2020	NUM
ajst-4909	25	4	,	,	PUNCT
ajst-4909	25	5	the	the	DET
ajst-4909	25	6	lamost	lamost	ADJ
ajst-4909	25	7	team	team	NOUN
ajst-4909	25	8	released	release	VERB
ajst-4909	25	9	the	the	DET
ajst-4909	25	10	dr7	dr7	NOUN
ajst-4909	25	11	catalog	catalog	NOUN
ajst-4909	25	12	,	,	PUNCT
ajst-4909	25	13	and	and	CCONJ
ajst-4909	25	14	in	in	ADP
ajst-4909	25	15	october	october	PROPN
ajst-4909	25	16	2021	2021	NUM
ajst-4909	25	17	released	release	VERB
ajst-4909	25	18	the	the	DET
ajst-4909	25	19	dr7	dr7	NOUN
ajst-4909	25	20	v2.0	v2.0	PUNCT
ajst-4909	25	21	version	version	NOUN
ajst-4909	25	22	,	,	PUNCT
ajst-4909	25	23	along	along	ADP
ajst-4909	25	24	with	with	ADP
ajst-4909	25	25	the	the	DET
ajst-4909	25	26	world	world	NOUN
ajst-4909	25	27	's	's	PART
ajst-4909	25	28	largest	large	ADJ
ajst-4909	25	29	stellar	stellar	ADJ
ajst-4909	25	30	parameter	parameter	NOUN
ajst-4909	25	31	catalog	catalog	NOUN
ajst-4909	25	32	of	of	ADP
ajst-4909	25	33	about	about	ADV
ajst-4909	25	34	6.91	6.91	NUM
ajst-4909	25	35	million	million	NUM
ajst-4909	25	36	groups	group	NOUN
ajst-4909	25	37	of	of	ADP
ajst-4909	25	38	stellar	stellar	ADJ
ajst-4909	25	39	spectral	spectral	ADJ
ajst-4909	25	40	parameters	parameter	NOUN
ajst-4909	25	41	,	,	PUNCT
ajst-4909	25	42	which	which	PRON
ajst-4909	25	43	means	mean	VERB
ajst-4909	25	44	a	a	DET
ajst-4909	25	45	large	large	ADJ
ajst-4909	25	46	number	number	NOUN
ajst-4909	25	47	of	of	ADP
ajst-4909	25	48	carbon	carbon	NOUN
ajst-4909	25	49	stars	star	NOUN
ajst-4909	25	50	were	be	AUX
ajst-4909	25	51	discovered	discover	VERB
ajst-4909	25	52	and	and	CCONJ
ajst-4909	25	53	the	the	DET
ajst-4909	25	54	low	low	ADJ
ajst-4909	25	55	-	-	PUNCT
ajst-4909	25	56	resolution	resolution	NOUN
ajst-4909	25	57	sky	sky	NOUN
ajst-4909	25	58	area	area	NOUN
ajst-4909	25	59	coverage	coverage	NOUN
ajst-4909	25	60	.	.	PUNCT
ajst-4909	26	1	spectral	spectral	ADJ
ajst-4909	26	2	dimensionality	dimensionality	NOUN
ajst-4909	26	3	reduction	reduction	NOUN
ajst-4909	26	4	is	be	AUX
ajst-4909	26	5	an	an	DET
ajst-4909	26	6	important	important	ADJ
ajst-4909	26	7	prerequisite	prerequisite	NOUN
ajst-4909	26	8	for	for	ADP
ajst-4909	26	9	accurate	accurate	ADJ
ajst-4909	26	10	classification	classification	NOUN
ajst-4909	26	11	.	.	PUNCT
ajst-4909	27	1	traditional	traditional	ADJ
ajst-4909	27	2	dimensionality	dimensionality	NOUN
ajst-4909	27	3	reduction	reduction	NOUN
ajst-4909	27	4	methods	method	NOUN
ajst-4909	27	5	such	such	ADJ
ajst-4909	27	6	as	as	ADP
ajst-4909	27	7	locally	locally	ADV
ajst-4909	27	8	linear	linear	NOUN
ajst-4909	27	9	embedding	embed	VERB
ajst-4909	27	10	[	[	X
ajst-4909	27	11	9	9	NUM
ajst-4909	27	12	]	]	PUNCT
ajst-4909	27	13	and	and	CCONJ
ajst-4909	27	14	linear	linear	ADJ
ajst-4909	27	15	discriminant	discriminant	ADJ
ajst-4909	27	16	analysis	analysis	NOUN
ajst-4909	27	17	[	[	X
ajst-4909	27	18	10	10	NUM
ajst-4909	27	19	]	]	PUNCT
ajst-4909	27	20	have	have	AUX
ajst-4909	27	21	been	be	AUX
ajst-4909	27	22	widely	widely	ADV
ajst-4909	27	23	applied	apply	VERB
ajst-4909	27	24	to	to	ADP
ajst-4909	27	25	spectral	spectral	ADJ
ajst-4909	27	26	dimensionality	dimensionality	NOUN
ajst-4909	27	27	reduction	reduction	NOUN
ajst-4909	27	28	and	and	CCONJ
ajst-4909	27	29	achieved	achieve	VERB
ajst-4909	27	30	good	good	ADJ
ajst-4909	27	31	results	result	NOUN
ajst-4909	27	32	.	.	PUNCT
ajst-4909	28	1	self	self	NOUN
ajst-4909	28	2	-	-	PUNCT
ajst-4909	28	3	encoders	encoder	NOUN
ajst-4909	28	4	[	[	X
ajst-4909	28	5	11	11	NUM
ajst-4909	28	6	]	]	PUNCT
ajst-4909	28	7	have	have	AUX
ajst-4909	28	8	also	also	ADV
ajst-4909	28	9	been	be	AUX
ajst-4909	28	10	widely	widely	ADV
ajst-4909	28	11	used	use	VERB
ajst-4909	28	12	for	for	ADP
ajst-4909	28	13	the	the	DET
ajst-4909	28	14	dimensionality	dimensionality	NOUN
ajst-4909	28	15	reduction	reduction	NOUN
ajst-4909	28	16	of	of	ADP
ajst-4909	28	17	data	datum	NOUN
ajst-4909	28	18	.	.	PUNCT
ajst-4909	29	1	117	117	NUM
ajst-4909	29	2	to	to	PART
ajst-4909	29	3	address	address	VERB
ajst-4909	29	4	the	the	DET
ajst-4909	29	5	crossover	crossover	NOUN
ajst-4909	29	6	problem	problem	NOUN
ajst-4909	29	7	of	of	ADP
ajst-4909	29	8	traditional	traditional	ADJ
ajst-4909	29	9	principal	principal	ADJ
ajst-4909	29	10	component	component	NOUN
ajst-4909	29	11	analysis	analysis	NOUN
ajst-4909	29	12	in	in	ADP
ajst-4909	29	13	the	the	DET
ajst-4909	29	14	low	low	ADJ
ajst-4909	29	15	-	-	PUNCT
ajst-4909	29	16	dimensional	dimensional	ADJ
ajst-4909	29	17	space	space	NOUN
ajst-4909	29	18	,	,	PUNCT
ajst-4909	29	19	this	this	DET
ajst-4909	29	20	paper	paper	NOUN
ajst-4909	29	21	investigates	investigate	VERB
ajst-4909	29	22	the	the	DET
ajst-4909	29	23	flow	flow	NOUN
ajst-4909	29	24	learning	learn	VERB
ajst-4909	29	25	algorithm	algorithm	PROPN
ajst-4909	29	26	t	t	PROPN
ajst-4909	29	27	-	-	PUNCT
ajst-4909	29	28	sne	sne	PROPN
ajst-4909	29	29	to	to	PART
ajst-4909	29	30	reduce	reduce	VERB
ajst-4909	29	31	the	the	DET
ajst-4909	29	32	dimensionality	dimensionality	NOUN
ajst-4909	29	33	of	of	ADP
ajst-4909	29	34	stellar	stellar	ADJ
ajst-4909	29	35	spectra	spectra	NOUN
ajst-4909	29	36	to	to	PART
ajst-4909	29	37	produce	produce	VERB
ajst-4909	29	38	more	more	ADJ
ajst-4909	29	39	obvious	obvious	ADJ
ajst-4909	29	40	classification	classification	NOUN
ajst-4909	29	41	boundaries	boundary	NOUN
ajst-4909	29	42	,	,	PUNCT
ajst-4909	29	43	and	and	CCONJ
ajst-4909	29	44	few	few	ADJ
ajst-4909	29	45	overlapping	overlap	VERB
ajst-4909	29	46	problems	problem	NOUN
ajst-4909	29	47	occur	occur	VERB
ajst-4909	29	48	in	in	ADP
ajst-4909	29	49	the	the	DET
ajst-4909	29	50	data	datum	NOUN
ajst-4909	29	51	,	,	PUNCT
ajst-4909	29	52	and	and	CCONJ
ajst-4909	29	53	the	the	DET
ajst-4909	29	54	trained	train	VERB
ajst-4909	29	55	classifier	classifier	NOUN
ajst-4909	29	56	has	have	VERB
ajst-4909	29	57	better	well	ADJ
ajst-4909	29	58	results	result	NOUN
ajst-4909	29	59	.	.	PUNCT
ajst-4909	30	1	2	2	X
ajst-4909	30	2	.	.	X
ajst-4909	30	3	dimensionality	dimensionality	NOUN
ajst-4909	30	4	reduction	reduction	NOUN
ajst-4909	30	5	and	and	CCONJ
ajst-4909	30	6	classification	classification	NOUN
ajst-4909	30	7	methods	method	NOUN
ajst-4909	30	8	2.1	2.1	NUM
ajst-4909	30	9	.	.	PUNCT
ajst-4909	31	1	t	t	PROPN
ajst-4909	31	2	-	-	PUNCT
ajst-4909	31	3	sne	sne	PROPN
ajst-4909	31	4	2.1.1	2.1.1	NUM
ajst-4909	31	5	.	.	PUNCT
ajst-4909	32	1	sub	sub	NOUN
ajst-4909	32	2	-	-	NOUN
ajst-4909	32	3	section	section	NOUN
ajst-4909	32	4	headings	heading	NOUN
ajst-4909	32	5	t	t	PROPN
ajst-4909	32	6	-	-	PUNCT
ajst-4909	32	7	sne	sne	NOUN
ajst-4909	32	8	[	[	X
ajst-4909	32	9	12	12	NUM
ajst-4909	32	10	]	]	PUNCT
ajst-4909	32	11	is	be	AUX
ajst-4909	32	12	a	a	DET
ajst-4909	32	13	nonlinear	nonlinear	ADJ
ajst-4909	32	14	dimensionality	dimensionality	NOUN
ajst-4909	32	15	reduction	reduction	NOUN
ajst-4909	32	16	algorithm	algorithm	NOUN
ajst-4909	32	17	based	base	VERB
ajst-4909	32	18	on	on	ADP
ajst-4909	32	19	sne	sne	PROPN
ajst-4909	32	20	,	,	PUNCT
ajst-4909	32	21	which	which	PRON
ajst-4909	32	22	is	be	AUX
ajst-4909	32	23	suitable	suitable	ADJ
ajst-4909	32	24	for	for	ADP
ajst-4909	32	25	reducing	reduce	VERB
ajst-4909	32	26	data	datum	NOUN
ajst-4909	32	27	to	to	ADP
ajst-4909	32	28	2	2	NUM
ajst-4909	32	29	-	-	SYM
ajst-4909	32	30	3	3	NUM
ajst-4909	32	31	dimensions	dimension	NOUN
ajst-4909	32	32	and	and	CCONJ
ajst-4909	32	33	thus	thus	ADV
ajst-4909	32	34	facilitating	facilitate	VERB
ajst-4909	32	35	visualization	visualization	NOUN
ajst-4909	32	36	.	.	PUNCT
ajst-4909	33	1	in	in	ADP
ajst-4909	33	2	the	the	DET
ajst-4909	33	3	sne	sne	NOUN
ajst-4909	33	4	algorithm	algorithm	NOUN
ajst-4909	33	5	,	,	PUNCT
ajst-4909	33	6	a	a	DET
ajst-4909	33	7	probability	probability	NOUN
ajst-4909	33	8	distribution	distribution	NOUN
ajst-4909	33	9	among	among	ADP
ajst-4909	33	10	highdimensional	highdimensional	ADJ
ajst-4909	33	11	objects	object	NOUN
ajst-4909	33	12	is	be	AUX
ajst-4909	33	13	first	first	ADV
ajst-4909	33	14	constructed	construct	VERB
ajst-4909	33	15	so	so	SCONJ
ajst-4909	33	16	that	that	SCONJ
ajst-4909	33	17	similar	similar	ADJ
ajst-4909	33	18	data	datum	NOUN
ajst-4909	33	19	have	have	VERB
ajst-4909	33	20	a	a	DET
ajst-4909	33	21	higher	high	ADJ
ajst-4909	33	22	probability	probability	NOUN
ajst-4909	33	23	of	of	ADP
ajst-4909	33	24	being	be	AUX
ajst-4909	33	25	selected	select	VERB
ajst-4909	33	26	,	,	PUNCT
ajst-4909	33	27	while	while	SCONJ
ajst-4909	33	28	data	datum	NOUN
ajst-4909	33	29	with	with	ADP
ajst-4909	33	30	large	large	ADJ
ajst-4909	33	31	differences	difference	NOUN
ajst-4909	33	32	have	have	VERB
ajst-4909	33	33	a	a	DET
ajst-4909	33	34	lower	low	ADJ
ajst-4909	33	35	probability	probability	NOUN
ajst-4909	33	36	of	of	ADP
ajst-4909	33	37	being	be	AUX
ajst-4909	33	38	selected	select	VERB
ajst-4909	33	39	.	.	PUNCT
ajst-4909	34	1	sne	sne	PROPN
ajst-4909	34	2	then	then	ADV
ajst-4909	34	3	constructs	construct	VERB
ajst-4909	34	4	the	the	DET
ajst-4909	34	5	probability	probability	NOUN
ajst-4909	34	6	distribution	distribution	NOUN
ajst-4909	34	7	of	of	ADP
ajst-4909	34	8	these	these	DET
ajst-4909	34	9	points	point	NOUN
ajst-4909	34	10	in	in	ADP
ajst-4909	34	11	a	a	DET
ajst-4909	34	12	low	low	ADJ
ajst-4909	34	13	-	-	PUNCT
ajst-4909	34	14	dimensional	dimensional	ADJ
ajst-4909	34	15	space	space	NOUN
ajst-4909	34	16	so	so	SCONJ
ajst-4909	34	17	that	that	SCONJ
ajst-4909	34	18	the	the	DET
ajst-4909	34	19	probability	probability	NOUN
ajst-4909	34	20	distributions	distribution	NOUN
ajst-4909	34	21	between	between	ADP
ajst-4909	34	22	the	the	DET
ajst-4909	34	23	high	high	ADV
ajst-4909	34	24	-	-	PUNCT
ajst-4909	34	25	dimensional	dimensional	ADJ
ajst-4909	34	26	and	and	CCONJ
ajst-4909	34	27	lowdimensional	lowdimensional	ADJ
ajst-4909	34	28	spaces	space	NOUN
ajst-4909	34	29	are	be	AUX
ajst-4909	34	30	as	as	ADV
ajst-4909	34	31	similar	similar	ADJ
ajst-4909	34	32	as	as	ADP
ajst-4909	34	33	possible	possible	ADJ
ajst-4909	34	34	.	.	PUNCT
ajst-4909	35	1	sne	sne	NOUN
ajst-4909	35	2	converts	convert	VERB
ajst-4909	35	3	the	the	DET
ajst-4909	35	4	high	high	ADV
ajst-4909	35	5	-	-	PUNCT
ajst-4909	35	6	dimensional	dimensional	ADJ
ajst-4909	35	7	euclidean	euclidean	ADJ
ajst-4909	35	8	distance	distance	NOUN
ajst-4909	35	9	between	between	ADP
ajst-4909	35	10	data	datum	NOUN
ajst-4909	35	11	points	point	NOUN
ajst-4909	35	12	into	into	ADP
ajst-4909	35	13	a	a	DET
ajst-4909	35	14	conditional	conditional	ADJ
ajst-4909	35	15	probability	probability	NOUN
ajst-4909	35	16	that	that	PRON
ajst-4909	35	17	represents	represent	VERB
ajst-4909	35	18	the	the	DET
ajst-4909	35	19	similarity	similarity	NOUN
ajst-4909	35	20	between	between	ADP
ajst-4909	35	21	the	the	DET
ajst-4909	35	22	data	datum	NOUN
ajst-4909	35	23	.	.	PUNCT
ajst-4909	36	1	the	the	DET
ajst-4909	36	2	conditional	conditional	ADJ
ajst-4909	36	3	probability	probability	NOUN
ajst-4909	36	4	|j	|j	NOUN
ajst-4909	36	5	ip	ip	NOUN
ajst-4909	36	6	between	between	ADP
ajst-4909	36	7	data	datum	NOUN
ajst-4909	36	8	sample	sample	NOUN
ajst-4909	36	9	points	point	NOUN
ajst-4909	36	10	ix	ix	ADV
ajst-4909	36	11	,	,	PUNCT
ajst-4909	36	12	jx	jx	PROPN
ajst-4909	36	13	is	be	AUX
ajst-4909	36	14	given	give	VERB
ajst-4909	36	15	by	by	ADP
ajst-4909	36	16	equation	equation	NOUN
ajst-4909	36	17	(	(	PUNCT
ajst-4909	36	18	1	1	NUM
ajst-4909	36	19	)	)	PUNCT
ajst-4909	36	20	.	.	PUNCT
ajst-4909	37	1			NOUN
ajst-4909	38	1			PUNCT
ajst-4909	38	2			NOUN
ajst-4909	38	3			NOUN
ajst-4909	38	4	2	2	NUM
ajst-4909	38	5	2	2	NUM
ajst-4909	38	6	|	|	CCONJ
ajst-4909	38	7	2	2	NUM
ajst-4909	38	8	2	2	NUM
ajst-4909	38	9	exp	exp	NOUN
ajst-4909	38	10	/2	/2	NOUN
ajst-4909	38	11	exp	exp	NOUN
ajst-4909	38	12	/2	/2	NOUN
ajst-4909	39	1	i	i	PRON
ajst-4909	39	2	j	j	INTJ
ajst-4909	40	1	i	i	PRON
ajst-4909	40	2	j	j	VERB
ajst-4909	41	1	i	i	PRON
ajst-4909	41	2	i	i	VERB
ajst-4909	42	1	k	k	INTJ
ajst-4909	43	1	i	i	PRON
ajst-4909	43	2	k	k	INTJ
ajst-4909	44	1	i	i	PRON
ajst-4909	44	2	x	x	X
ajst-4909	45	1	x	x	X
ajst-4909	45	2	p	p	X
ajst-4909	45	3	x	x	X
ajst-4909	45	4	x	x	X
ajst-4909	45	5			PROPN
ajst-4909	45	6			PROPN
ajst-4909	45	7			PROPN
ajst-4909	45	8			PROPN
ajst-4909	45	9			PROPN
ajst-4909	45	10			PROPN
ajst-4909	45	11			PROPN
ajst-4909	45	12			PROPN
ajst-4909	45	13	�	�	PROPN
ajst-4909	45	14	�	�	PROPN
ajst-4909	45	15	�	�	PROPN
ajst-4909	45	16	�	�	PROPN
ajst-4909	45	17	(	(	PUNCT
ajst-4909	45	18	1	1	NUM
ajst-4909	45	19	)	)	PUNCT
ajst-4909	45	20	i	i	NOUN
ajst-4909	45	21	is	be	AUX
ajst-4909	45	22	the	the	DET
ajst-4909	45	23	gaussian	gaussian	ADJ
ajst-4909	45	24	variance	variance	NOUN
ajst-4909	45	25	centered	center	VERB
ajst-4909	45	26	on	on	ADP
ajst-4909	45	27	data	datum	NOUN
ajst-4909	45	28	point	point	NOUN
ajst-4909	45	29	ix	ix	ADV
ajst-4909	45	30	.	.	PUNCT
ajst-4909	46	1	for	for	ADP
ajst-4909	46	2	the	the	DET
ajst-4909	46	3	low	low	ADV
ajst-4909	46	4	-	-	PUNCT
ajst-4909	46	5	dimensional	dimensional	ADJ
ajst-4909	46	6	counterparts	counterpart	NOUN
ajst-4909	46	7	iy	iy	PROPN
ajst-4909	46	8	and	and	CCONJ
ajst-4909	46	9	jy	jy	PROPN
ajst-4909	46	10	of	of	ADP
ajst-4909	46	11	the	the	DET
ajst-4909	46	12	high	high	ADV
ajst-4909	46	13	-	-	PUNCT
ajst-4909	46	14	dimensional	dimensional	ADJ
ajst-4909	46	15	data	datum	NOUN
ajst-4909	46	16	points	point	NOUN
ajst-4909	46	17	ix	ix	ADV
ajst-4909	46	18	and	and	CCONJ
ajst-4909	46	19	jx	jx	PROPN
ajst-4909	46	20	,	,	PUNCT
ajst-4909	46	21	a	a	DET
ajst-4909	46	22	similar	similar	ADJ
ajst-4909	46	23	conditional	conditional	ADJ
ajst-4909	46	24	probability	probability	NOUN
ajst-4909	46	25	can	can	AUX
ajst-4909	46	26	be	be	AUX
ajst-4909	46	27	calculated	calculate	VERB
ajst-4909	46	28	|j	|j	NOUN
ajst-4909	46	29	iq	iq	NOUN
ajst-4909	46	30	.	.	PUNCT
ajst-4909	47	1			NOUN
ajst-4909	48	1			PUNCT
ajst-4909	48	2			NOUN
ajst-4909	48	3			PROPN
ajst-4909	48	4	2	2	NUM
ajst-4909	48	5	|	|	CCONJ
ajst-4909	48	6	2	2	NUM
ajst-4909	48	7	exp	exp	NOUN
ajst-4909	48	8	exp	exp	NOUN
ajst-4909	49	1	i	i	PRON
ajst-4909	49	2	j	j	PROPN
ajst-4909	50	1	j	j	NOUN
ajst-4909	51	1	i	i	PRON
ajst-4909	52	1	i	i	VERB
ajst-4909	52	2	k	k	VERB
ajst-4909	53	1	k	k	PROPN
ajst-4909	54	1	i	i	PRON
ajst-4909	54	2	y	y	VERB
ajst-4909	54	3	y	y	INTJ
ajst-4909	54	4	q	q	PROPN
ajst-4909	54	5	y	y	PROPN
ajst-4909	54	6	y	y	PROPN
ajst-4909	54	7			PROPN
ajst-4909	54	8			PROPN
ajst-4909	54	9			PROPN
ajst-4909	54	10			PROPN
ajst-4909	54	11			PROPN
ajst-4909	54	12			PROPN
ajst-4909	54	13	�	�	PROPN
ajst-4909	54	14	�	�	PROPN
ajst-4909	54	15	�	�	PROPN
ajst-4909	54	16	�	�	PROPN
ajst-4909	54	17	(	(	PUNCT
ajst-4909	54	18	2	2	NUM
ajst-4909	54	19	)	)	PUNCT
ajst-4909	54	20	the	the	DET
ajst-4909	54	21	goal	goal	NOUN
ajst-4909	54	22	of	of	ADP
ajst-4909	54	23	sne	sne	NOUN
ajst-4909	54	24	is	be	AUX
ajst-4909	54	25	to	to	PART
ajst-4909	54	26	minimize	minimize	VERB
ajst-4909	54	27	the	the	DET
ajst-4909	54	28	difference	difference	NOUN
ajst-4909	54	29	in	in	ADP
ajst-4909	54	30	conditional	conditional	ADJ
ajst-4909	54	31	probabilities	probability	NOUN
ajst-4909	54	32	.	.	PUNCT
ajst-4909	55	1	to	to	PART
ajst-4909	55	2	compute	compute	VERB
ajst-4909	55	3	the	the	DET
ajst-4909	55	4	minimum	minimum	NOUN
ajst-4909	55	5	of	of	ADP
ajst-4909	55	6	the	the	DET
ajst-4909	55	7	conditional	conditional	ADJ
ajst-4909	55	8	probability	probability	NOUN
ajst-4909	55	9	difference	difference	NOUN
ajst-4909	55	10	,	,	PUNCT
ajst-4909	55	11	sne	sne	NOUN
ajst-4909	55	12	minimizes	minimize	VERB
ajst-4909	55	13	the	the	DET
ajst-4909	55	14	kl	kl	PROPN
ajst-4909	55	15	distance	distance	NOUN
ajst-4909	55	16	by	by	ADP
ajst-4909	55	17	the	the	DET
ajst-4909	55	18	gradient	gradient	ADJ
ajst-4909	55	19	descent	descent	NOUN
ajst-4909	55	20	method	method	NOUN
ajst-4909	55	21	.	.	PUNCT
ajst-4909	56	1	however	however	ADV
ajst-4909	56	2	,	,	PUNCT
ajst-4909	56	3	the	the	DET
ajst-4909	56	4	cost	cost	NOUN
ajst-4909	56	5	function	function	NOUN
ajst-4909	56	6	of	of	ADP
ajst-4909	56	7	sne	sne	PROPN
ajst-4909	56	8	focuses	focus	VERB
ajst-4909	56	9	on	on	ADP
ajst-4909	56	10	the	the	DET
ajst-4909	56	11	local	local	ADJ
ajst-4909	56	12	structure	structure	NOUN
ajst-4909	56	13	of	of	ADP
ajst-4909	56	14	the	the	DET
ajst-4909	56	15	data	datum	NOUN
ajst-4909	56	16	in	in	ADP
ajst-4909	56	17	the	the	DET
ajst-4909	56	18	mapping	mapping	NOUN
ajst-4909	56	19	,	,	PUNCT
ajst-4909	56	20	and	and	CCONJ
ajst-4909	56	21	the	the	DET
ajst-4909	56	22	optimization	optimization	NOUN
ajst-4909	56	23	of	of	ADP
ajst-4909	56	24	this	this	DET
ajst-4909	56	25	function	function	NOUN
ajst-4909	56	26	is	be	AUX
ajst-4909	56	27	difficult	difficult	ADJ
ajst-4909	56	28	to	to	PART
ajst-4909	56	29	achieve	achieve	VERB
ajst-4909	56	30	,	,	PUNCT
ajst-4909	56	31	so	so	SCONJ
ajst-4909	56	32	it	it	PRON
ajst-4909	56	33	needs	need	VERB
ajst-4909	56	34	to	to	PART
ajst-4909	56	35	be	be	AUX
ajst-4909	56	36	improved	improve	VERB
ajst-4909	56	37	in	in	ADP
ajst-4909	56	38	the	the	DET
ajst-4909	56	39	way	way	NOUN
ajst-4909	56	40	of	of	ADP
ajst-4909	56	41	implementation	implementation	NOUN
ajst-4909	56	42	.	.	PUNCT
ajst-4909	57	1	the	the	DET
ajst-4909	57	2	t	t	PROPN
ajst-4909	57	3	-	-	PUNCT
ajst-4909	57	4	sne	sne	NOUN
ajst-4909	57	5	uses	use	VERB
ajst-4909	57	6	a	a	DET
ajst-4909	57	7	t	t	NOUN
ajst-4909	57	8	-	-	PUNCT
ajst-4909	57	9	distribution	distribution	NOUN
ajst-4909	57	10	in	in	ADP
ajst-4909	57	11	the	the	DET
ajst-4909	57	12	low	low	ADJ
ajst-4909	57	13	-	-	PUNCT
ajst-4909	57	14	dimensional	dimensional	ADJ
ajst-4909	57	15	space	space	NOUN
ajst-4909	57	16	that	that	PRON
ajst-4909	57	17	focuses	focus	VERB
ajst-4909	57	18	more	more	ADJ
ajst-4909	57	19	on	on	ADP
ajst-4909	57	20	the	the	DET
ajst-4909	57	21	long	long	ADJ
ajst-4909	57	22	-	-	PUNCT
ajst-4909	57	23	tail	tail	NOUN
ajst-4909	57	24	distribution	distribution	NOUN
ajst-4909	57	25	instead	instead	ADV
ajst-4909	57	26	of	of	ADP
ajst-4909	57	27	the	the	DET
ajst-4909	57	28	gaussian	gaussian	ADJ
ajst-4909	57	29	distribution	distribution	NOUN
ajst-4909	57	30	to	to	PART
ajst-4909	57	31	represent	represent	VERB
ajst-4909	57	32	the	the	DET
ajst-4909	57	33	similarity	similarity	NOUN
ajst-4909	57	34	between	between	ADP
ajst-4909	57	35	two	two	NUM
ajst-4909	57	36	points	point	NOUN
ajst-4909	57	37	.	.	PUNCT
ajst-4909	58	1	for	for	ADP
ajst-4909	58	2	points	point	NOUN
ajst-4909	58	3	with	with	ADP
ajst-4909	58	4	greater	great	ADJ
ajst-4909	58	5	similarity	similarity	NOUN
ajst-4909	58	6	,	,	PUNCT
ajst-4909	58	7	the	the	DET
ajst-4909	58	8	distance	distance	NOUN
ajst-4909	58	9	of	of	ADP
ajst-4909	58	10	the	the	DET
ajst-4909	58	11	t	t	NOUN
ajst-4909	58	12	-	-	PUNCT
ajst-4909	58	13	distribution	distribution	NOUN
ajst-4909	58	14	in	in	ADP
ajst-4909	58	15	the	the	DET
ajst-4909	58	16	low	low	ADJ
ajst-4909	58	17	-	-	PUNCT
ajst-4909	58	18	dimensional	dimensional	ADJ
ajst-4909	58	19	space	space	NOUN
ajst-4909	58	20	is	be	AUX
ajst-4909	58	21	slightly	slightly	ADV
ajst-4909	58	22	smaller	small	ADJ
ajst-4909	58	23	,	,	PUNCT
ajst-4909	58	24	while	while	SCONJ
ajst-4909	58	25	for	for	ADP
ajst-4909	58	26	points	point	NOUN
ajst-4909	58	27	with	with	ADP
ajst-4909	58	28	low	low	ADJ
ajst-4909	58	29	similarity	similarity	NOUN
ajst-4909	58	30	,	,	PUNCT
ajst-4909	58	31	the	the	DET
ajst-4909	58	32	distance	distance	NOUN
ajst-4909	58	33	of	of	ADP
ajst-4909	58	34	the	the	DET
ajst-4909	58	35	t	t	NOUN
ajst-4909	58	36	-	-	PUNCT
ajst-4909	58	37	distribution	distribution	NOUN
ajst-4909	58	38	in	in	ADP
ajst-4909	58	39	the	the	DET
ajst-4909	58	40	low	low	ADJ
ajst-4909	58	41	-	-	PUNCT
ajst-4909	58	42	dimensional	dimensional	ADJ
ajst-4909	58	43	space	space	NOUN
ajst-4909	58	44	needs	need	VERB
ajst-4909	58	45	to	to	PART
ajst-4909	58	46	be	be	AUX
ajst-4909	58	47	farther	farth	ADJ
ajst-4909	58	48	.	.	PUNCT
ajst-4909	59	1	this	this	DET
ajst-4909	59	2	distribution	distribution	NOUN
ajst-4909	59	3	can	can	AUX
ajst-4909	59	4	effectively	effectively	ADV
ajst-4909	59	5	handle	handle	VERB
ajst-4909	59	6	the	the	DET
ajst-4909	59	7	outlier	outlier	ADJ
ajst-4909	59	8	points	point	NOUN
ajst-4909	59	9	in	in	ADP
ajst-4909	59	10	the	the	DET
ajst-4909	59	11	data	datum	NOUN
ajst-4909	59	12	,	,	PUNCT
ajst-4909	59	13	i.e.	i.e.	X
ajst-4909	59	14	,	,	PUNCT
ajst-4909	59	15	anomalous	anomalous	ADJ
ajst-4909	59	16	data	datum	NOUN
ajst-4909	59	17	,	,	PUNCT
ajst-4909	59	18	to	to	PART
ajst-4909	59	19	improve	improve	VERB
ajst-4909	59	20	the	the	DET
ajst-4909	59	21	dimensionality	dimensionality	NOUN
ajst-4909	59	22	reduction	reduction	NOUN
ajst-4909	59	23	effect	effect	NOUN
ajst-4909	59	24	.	.	PUNCT
ajst-4909	60	1	2.2	2.2	NUM
ajst-4909	60	2	.	.	PUNCT
ajst-4909	61	1	knn	knn	PROPN
ajst-4909	61	2	algorithm	algorithm	PROPN
ajst-4909	61	3	based	base	VERB
ajst-4909	61	4	on	on	ADP
ajst-4909	61	5	attribute	attribute	NOUN
ajst-4909	61	6	value	value	NOUN
ajst-4909	61	7	correlation	correlation	NOUN
ajst-4909	61	8	distance	distance	NOUN
ajst-4909	61	9	the	the	DET
ajst-4909	61	10	k	k	NOUN
ajst-4909	61	11	-	-	PUNCT
ajst-4909	61	12	nearest	near	ADJ
ajst-4909	61	13	neighbor	neighbor	NOUN
ajst-4909	61	14	algorithm	algorithm	NOUN
ajst-4909	61	15	based	base	VERB
ajst-4909	61	16	on	on	ADP
ajst-4909	61	17	attribute	attribute	NOUN
ajst-4909	61	18	-	-	PUNCT
ajst-4909	61	19	value	value	NOUN
ajst-4909	61	20	related	relate	VERB
ajst-4909	61	21	distance	distance	NOUN
ajst-4909	61	22	is	be	AUX
ajst-4909	61	23	an	an	DET
ajst-4909	61	24	improved	improved	ADJ
ajst-4909	61	25	algorithm	algorithm	NOUN
ajst-4909	61	26	for	for	ADP
ajst-4909	61	27	the	the	DET
ajst-4909	61	28	traditional	traditional	ADJ
ajst-4909	61	29	k	k	NOUN
ajst-4909	61	30	-	-	PUNCT
ajst-4909	61	31	nearest	near	ADJ
ajst-4909	61	32	neighbor	neighbor	NOUN
ajst-4909	61	33	algorithm	algorithm	NOUN
ajst-4909	61	34	in	in	ADP
ajst-4909	61	35	terms	term	NOUN
ajst-4909	61	36	of	of	ADP
ajst-4909	61	37	distance	distance	NOUN
ajst-4909	61	38	function	function	NOUN
ajst-4909	61	39	.	.	PUNCT
ajst-4909	62	1	the	the	DET
ajst-4909	62	2	algorithm	algorithm	NOUN
ajst-4909	62	3	first	first	ADV
ajst-4909	62	4	calculates	calculate	VERB
ajst-4909	62	5	the	the	DET
ajst-4909	62	6	distance	distance	NOUN
ajst-4909	62	7	between	between	ADP
ajst-4909	62	8	the	the	DET
ajst-4909	62	9	samples	sample	NOUN
ajst-4909	62	10	to	to	PART
ajst-4909	62	11	be	be	AUX
ajst-4909	62	12	classified	classify	VERB
ajst-4909	62	13	and	and	CCONJ
ajst-4909	62	14	the	the	DET
ajst-4909	62	15	training	training	NOUN
ajst-4909	62	16	samples	sample	NOUN
ajst-4909	62	17	of	of	ADP
ajst-4909	62	18	known	know	VERB
ajst-4909	62	19	classes	class	NOUN
ajst-4909	62	20	using	use	VERB
ajst-4909	62	21	the	the	DET
ajst-4909	62	22	improved	improve	VERB
ajst-4909	62	23	distance	distance	NOUN
ajst-4909	62	24	function	function	NOUN
ajst-4909	62	25	,	,	PUNCT
ajst-4909	62	26	and	and	CCONJ
ajst-4909	62	27	then	then	ADV
ajst-4909	62	28	selects	select	VERB
ajst-4909	62	29	the	the	DET
ajst-4909	62	30	first	first	ADJ
ajst-4909	62	31	k	k	ADJ
ajst-4909	62	32	minimum	minimum	ADJ
ajst-4909	62	33	distances	distance	NOUN
ajst-4909	62	34	,	,	PUNCT
ajst-4909	62	35	and	and	CCONJ
ajst-4909	62	36	the	the	DET
ajst-4909	62	37	samples	sample	NOUN
ajst-4909	62	38	with	with	ADP
ajst-4909	62	39	k	k	PROPN
ajst-4909	62	40	minimum	minimum	ADJ
ajst-4909	62	41	distances	distance	NOUN
ajst-4909	62	42	are	be	AUX
ajst-4909	62	43	called	call	VERB
ajst-4909	62	44	neighbors	neighbor	NOUN
ajst-4909	62	45	,	,	PUNCT
ajst-4909	62	46	and	and	CCONJ
ajst-4909	62	47	determines	determine	VERB
ajst-4909	62	48	the	the	DET
ajst-4909	62	49	kind	kind	NOUN
ajst-4909	62	50	of	of	ADP
ajst-4909	62	51	samples	sample	NOUN
ajst-4909	62	52	to	to	PART
ajst-4909	62	53	be	be	AUX
ajst-4909	62	54	classified	classify	VERB
ajst-4909	62	55	according	accord	VERB
ajst-4909	62	56	to	to	ADP
ajst-4909	62	57	the	the	DET
ajst-4909	62	58	class	class	NOUN
ajst-4909	62	59	confidence	confidence	NOUN
ajst-4909	62	60	.	.	PUNCT
ajst-4909	63	1	the	the	DET
ajst-4909	63	2	algorithm	algorithm	NOUN
ajst-4909	63	3	pays	pay	VERB
ajst-4909	63	4	more	more	ADJ
ajst-4909	63	5	attention	attention	NOUN
ajst-4909	63	6	to	to	ADP
ajst-4909	63	7	the	the	DET
ajst-4909	63	8	statistical	statistical	ADJ
ajst-4909	63	9	relevance	relevance	NOUN
ajst-4909	63	10	of	of	ADP
ajst-4909	63	11	the	the	DET
ajst-4909	63	12	data	datum	NOUN
ajst-4909	63	13	rather	rather	ADV
ajst-4909	63	14	than	than	ADP
ajst-4909	63	15	just	just	ADV
ajst-4909	63	16	measuring	measure	VERB
ajst-4909	63	17	the	the	DET
ajst-4909	63	18	euclidean	euclidean	ADJ
ajst-4909	63	19	distance	distance	NOUN
ajst-4909	63	20	between	between	ADP
ajst-4909	63	21	the	the	DET
ajst-4909	63	22	data	datum	NOUN
ajst-4909	63	23	.	.	PUNCT
ajst-4909	64	1	the	the	DET
ajst-4909	64	2	improved	improve	VERB
ajst-4909	64	3	distance	distance	NOUN
ajst-4909	64	4	function	function	NOUN
ajst-4909	64	5	is	be	AUX
ajst-4909	64	6	the	the	DET
ajst-4909	64	7	correlation	correlation	NOUN
ajst-4909	64	8	distance	distance	NOUN
ajst-4909	64	9	function	function	NOUN
ajst-4909	64	10	.	.	PUNCT
ajst-4909	65	1	the	the	DET
ajst-4909	65	2	correlation	correlation	NOUN
ajst-4909	65	3	coefficients	coefficient	NOUN
ajst-4909	65	4	of	of	ADP
ajst-4909	65	5	samples	sample	NOUN
ajst-4909	65	6	1x	1x	NUM
ajst-4909	65	7	and	and	CCONJ
ajst-4909	65	8	2x	2x	NUM
ajst-4909	65	9	are	be	AUX
ajst-4909	65	10			NOUN
ajst-4909	65	11			PUNCT
ajst-4909	65	12			NOUN
ajst-4909	65	13			SYM
ajst-4909	65	14			NOUN
ajst-4909	65	15	1	1	NOUN
ajst-4909	65	16	2	2	NUM
ajst-4909	65	17	1	1	NUM
ajst-4909	65	18	2	2	NUM
ajst-4909	65	19	,	,	PUNCT
ajst-4909	65	20	1	1	NUM
ajst-4909	65	21	2	2	NUM
ajst-4909	65	22	,	,	PUNCT
ajst-4909	65	23	x	x	PUNCT
ajst-4909	65	24	x	x	X
ajst-4909	65	25	cov	cov	NOUN
ajst-4909	65	26	x	x	PUNCT
ajst-4909	65	27	x	x	PUNCT
ajst-4909	66	1	d	d	NOUN
ajst-4909	66	2	x	x	X
ajst-4909	66	3	d	d	X
ajst-4909	66	4	x	x	X
ajst-4909	66	5			ADP
ajst-4909	66	6			PROPN
ajst-4909	66	7	(	(	PUNCT
ajst-4909	66	8	3	3	X
ajst-4909	66	9	)	)	PUNCT
ajst-4909	66	10	define	define	VERB
ajst-4909	66	11	the	the	DET
ajst-4909	66	12	correlation	correlation	NOUN
ajst-4909	66	13	distance	distance	NOUN
ajst-4909	66	14	between	between	ADP
ajst-4909	66	15	samples	sample	NOUN
ajst-4909	66	16	1x	1x	NUM
ajst-4909	66	17	and	and	CCONJ
ajst-4909	66	18	2x	2x	NUM
ajst-4909	66	19	as	as	ADP
ajst-4909	66	20			NOUN
ajst-4909	66	21			PROPN
ajst-4909	66	22			NOUN
ajst-4909	66	23			SYM
ajst-4909	66	24			NOUN
ajst-4909	66	25			SYM
ajst-4909	66	26			NOUN
ajst-4909	66	27			PUNCT
ajst-4909	66	28			NOUN
ajst-4909	67	1			NOUN
ajst-4909	67	2			PROPN
ajst-4909	67	3			PROPN
ajst-4909	67	4			NOUN
ajst-4909	67	5			SYM
ajst-4909	67	6			NOUN
ajst-4909	67	7	1	1	NOUN
ajst-4909	67	8	2	2	NUM
ajst-4909	67	9	1	1	NUM
ajst-4909	67	10	2	2	NUM
ajst-4909	67	11	1	1	NUM
ajst-4909	67	12	21	21	NUM
ajst-4909	67	13	2	2	NUM
ajst-4909	67	14	1	1	NUM
ajst-4909	67	15	2	2	NUM
ajst-4909	67	16	,	,	PUNCT
ajst-4909	67	17	1	1	NUM
ajst-4909	67	18	2	2	NUM
ajst-4909	67	19	1	1	NUM
ajst-4909	67	20	2	2	NUM
ajst-4909	67	21	,	,	PUNCT
ajst-4909	67	22	,	,	PUNCT
ajst-4909	67	23	1	1	NUM
ajst-4909	67	24	1	1	NUM
ajst-4909	67	25	1x	1x	NUM
ajst-4909	67	26	x	x	PUNCT
ajst-4909	67	27	e	e	NOUN
ajst-4909	67	28	x	x	X
ajst-4909	67	29	ex	ex	X
ajst-4909	67	30	x	x	PUNCT
ajst-4909	67	31	excov	excov	VERB
ajst-4909	67	32	x	x	PUNCT
ajst-4909	67	33	x	x	PUNCT
ajst-4909	68	1	d	d	NOUN
ajst-4909	68	2	x	x	X
ajst-4909	68	3	x	x	PUNCT
ajst-4909	68	4	d	d	NOUN
ajst-4909	68	5	x	x	X
ajst-4909	68	6	d	d	NOUN
ajst-4909	68	7	x	x	X
ajst-4909	68	8	d	d	NOUN
ajst-4909	68	9	x	x	X
ajst-4909	68	10	d	d	X
ajst-4909	68	11	x	x	X
ajst-4909	68	12			ADP
ajst-4909	68	13			PROPN
ajst-4909	68	14			PROPN
ajst-4909	68	15			PROPN
ajst-4909	68	16			PROPN
ajst-4909	68	17			PROPN
ajst-4909	68	18			PROPN
ajst-4909	68	19			PROPN
ajst-4909	68	20			NOUN
ajst-4909	68	21	(	(	PUNCT
ajst-4909	68	22	4	4	NUM
ajst-4909	68	23	)	)	PUNCT
ajst-4909	68	24	the	the	PRON
ajst-4909	68	25	smaller	small	ADJ
ajst-4909	68	26	the	the	DET
ajst-4909	68	27	correlation	correlation	NOUN
ajst-4909	68	28	distance	distance	NOUN
ajst-4909	68	29	between	between	ADP
ajst-4909	68	30	the	the	DET
ajst-4909	68	31	samples	sample	NOUN
ajst-4909	68	32	,	,	PUNCT
ajst-4909	68	33	the	the	PRON
ajst-4909	68	34	greater	great	ADJ
ajst-4909	68	35	the	the	DET
ajst-4909	68	36	correlation	correlation	NOUN
ajst-4909	68	37	between	between	ADP
ajst-4909	68	38	the	the	DET
ajst-4909	68	39	two	two	NUM
ajst-4909	68	40	samples	sample	NOUN
ajst-4909	68	41	.	.	PUNCT
ajst-4909	69	1	the	the	DET
ajst-4909	69	2	class	class	NOUN
ajst-4909	69	3	confidence	confidence	NOUN
ajst-4909	69	4	is	be	AUX
ajst-4909	69	5	defined	define	VERB
ajst-4909	69	6	as	as	ADP
ajst-4909	69	7	:	:	PUNCT
ajst-4909	69	8	rc	rc	PROPN
ajst-4909	69	9	is	be	AUX
ajst-4909	69	10	the	the	DET
ajst-4909	69	11	category	category	NOUN
ajst-4909	69	12	,	,	PUNCT
ajst-4909	69	13	testx	testx	NOUN
ajst-4909	69	14	is	be	AUX
ajst-4909	69	15	the	the	DET
ajst-4909	69	16	sample	sample	NOUN
ajst-4909	69	17	to	to	PART
ajst-4909	69	18	be	be	AUX
ajst-4909	69	19	classified	classify	VERB
ajst-4909	69	20	,	,	PUNCT
ajst-4909	69	21	rx	rx	VERB
ajst-4909	69	22	is	be	AUX
ajst-4909	69	23	the	the	DET
ajst-4909	69	24	number	number	NOUN
ajst-4909	69	25	of	of	ADP
ajst-4909	69	26	samples	sample	NOUN
ajst-4909	69	27	belonging	belong	VERB
ajst-4909	69	28	to	to	ADP
ajst-4909	69	29	rc	rc	PROPN
ajst-4909	69	30	in	in	ADP
ajst-4909	69	31	the	the	DET
ajst-4909	69	32	neighborhood	neighborhood	NOUN
ajst-4909	69	33	,	,	PUNCT
ajst-4909	69	34	n	n	X
ajst-4909	69	35	is	be	AUX
ajst-4909	69	36	the	the	DET
ajst-4909	69	37	total	total	ADJ
ajst-4909	69	38	number	number	NOUN
ajst-4909	69	39	of	of	ADP
ajst-4909	69	40	samples	sample	NOUN
ajst-4909	69	41	in	in	ADP
ajst-4909	69	42	the	the	DET
ajst-4909	69	43	neighborhood	neighborhood	NOUN
ajst-4909	69	44	,	,	PUNCT
ajst-4909	69	45	and	and	CCONJ
ajst-4909	69	46	rn	rn	PROPN
ajst-4909	69	47	is	be	AUX
ajst-4909	69	48	the	the	DET
ajst-4909	69	49	number	number	NOUN
ajst-4909	69	50	of	of	ADP
ajst-4909	69	51	samples	sample	NOUN
ajst-4909	69	52	belonging	belong	VERB
ajst-4909	69	53	to	to	ADP
ajst-4909	69	54	rc	rc	PROPN
ajst-4909	69	55	in	in	ADP
ajst-4909	69	56	the	the	DET
ajst-4909	69	57	neighborhood	neighborhood	NOUN
ajst-4909	69	58	.	.	PUNCT
ajst-4909	70	1	rn	rn	PROPN
ajst-4909	70	2	is	be	AUX
ajst-4909	70	3	the	the	DET
ajst-4909	70	4	number	number	NOUN
ajst-4909	70	5	of	of	ADP
ajst-4909	70	6	samples	sample	NOUN
ajst-4909	70	7	belonging	belong	VERB
ajst-4909	70	8	to	to	ADP
ajst-4909	70	9	rc	rc	PROPN
ajst-4909	70	10	in	in	ADP
ajst-4909	70	11	the	the	DET
ajst-4909	70	12	neighboring	neighboring	NOUN
ajst-4909	70	13	points	point	NOUN
ajst-4909	70	14	.	.	PUNCT
ajst-4909	71	1			NOUN
ajst-4909	71	2	,r	,r	VERB
ajst-4909	71	3	testt	testt	NOUN
ajst-4909	71	4	c	c	NOUN
ajst-4909	71	5	x	x	X
ajst-4909	71	6	is	be	AUX
ajst-4909	71	7	the	the	DET
ajst-4909	71	8	class	class	NOUN
ajst-4909	71	9	reliability	reliability	NOUN
ajst-4909	71	10	of	of	ADP
ajst-4909	71	11	testx	testx	NOUN
ajst-4909	71	12	on	on	ADP
ajst-4909	71	13	rc	rc	PROPN
ajst-4909	71	14	reliability	reliability	NOUN
ajst-4909	71	15	,	,	PUNCT
ajst-4909	71	16	denoted	denote	VERB
ajst-4909	71	17	as	as	ADP
ajst-4909	71	18			NOUN
ajst-4909	72	1			PROPN
ajst-4909	72	2			NOUN
ajst-4909	72	3			PUNCT
ajst-4909	72	4	1	1	NUM
ajst-4909	72	5	1	1	NUM
ajst-4909	72	6	,	,	PUNCT
ajst-4909	72	7	,	,	PUNCT
ajst-4909	72	8	rn	rn	PROPN
ajst-4909	72	9	r	r	NOUN
ajst-4909	72	10	r	r	NOUN
ajst-4909	72	11	test	test	NOUN
ajst-4909	72	12	test	test	NOUN
ajst-4909	72	13	r	r	NOUN
ajst-4909	72	14	r	r	NOUN
ajst-4909	72	15	n	n	NOUN
ajst-4909	72	16	n	n	NOUN
ajst-4909	72	17	t	t	NOUN
ajst-4909	72	18	c	c	NOUN
ajst-4909	72	19	x	x	PUNCT
ajst-4909	73	1	d	d	NOUN
ajst-4909	73	2	x	x	SYM
ajst-4909	73	3	x	x	NOUN
ajst-4909	73	4	n	n	CCONJ
ajst-4909	73	5	n	n	CCONJ
ajst-4909	73	6			PROPN
ajst-4909	73	7			PROPN
ajst-4909	73	8			NOUN
ajst-4909	73	9			X
ajst-4909	73	10	(	(	PUNCT
ajst-4909	73	11	5	5	X
ajst-4909	73	12	)	)	PUNCT
ajst-4909	73	13	the	the	PRON
ajst-4909	73	14	more	more	ADV
ajst-4909	73	15	the	the	DET
ajst-4909	73	16	number	number	NOUN
ajst-4909	73	17	of	of	ADP
ajst-4909	73	18	samples	sample	NOUN
ajst-4909	73	19	belonging	belong	VERB
ajst-4909	73	20	to	to	ADP
ajst-4909	73	21	class	class	PROPN
ajst-4909	73	22	rc	rc	PROPN
ajst-4909	73	23	and	and	CCONJ
ajst-4909	73	24	the	the	DET
ajst-4909	73	25	smaller	small	ADJ
ajst-4909	73	26	the	the	DET
ajst-4909	73	27	class	class	NOUN
ajst-4909	73	28	confidence	confidence	NOUN
ajst-4909	73	29	,	,	PUNCT
ajst-4909	73	30	the	the	PRON
ajst-4909	73	31	more	more	ADV
ajst-4909	73	32	likely	likely	ADJ
ajst-4909	73	33	the	the	DET
ajst-4909	73	34	samples	sample	NOUN
ajst-4909	73	35	to	to	PART
ajst-4909	73	36	be	be	AUX
ajst-4909	73	37	classified	classify	VERB
ajst-4909	73	38	the	the	DET
ajst-4909	73	39	more	more	ADV
ajst-4909	73	40	likely	likely	ADJ
ajst-4909	73	41	to	to	PART
ajst-4909	73	42	be	be	AUX
ajst-4909	73	43	labeled	label	VERB
ajst-4909	73	44	as	as	ADP
ajst-4909	73	45	rc	rc	PROPN
ajst-4909	73	46	class	class	NOUN
ajst-4909	73	47	,	,	PUNCT
ajst-4909	73	48	the	the	DET
ajst-4909	73	49	more	more	ADV
ajst-4909	73	50	likely	likely	ADJ
ajst-4909	73	51	to	to	PART
ajst-4909	73	52	be	be	AUX
ajst-4909	73	53	labeled	label	VERB
ajst-4909	73	54	as	as	ADP
ajst-4909	73	55	rc	rc	PROPN
ajst-4909	73	56	class	class	NOUN
ajst-4909	73	57	.	.	PUNCT
ajst-4909	74	1	in	in	ADP
ajst-4909	74	2	summary	summary	NOUN
ajst-4909	74	3	,	,	PUNCT
ajst-4909	74	4	the	the	DET
ajst-4909	74	5	algorithm	algorithm	NOUN
ajst-4909	74	6	based	base	VERB
ajst-4909	74	7	on	on	ADP
ajst-4909	74	8	the	the	DET
ajst-4909	74	9	attribute	attribute	NOUN
ajst-4909	74	10	value	value	NOUN
ajst-4909	74	11	correlation	correlation	NOUN
ajst-4909	74	12	distance	distance	NOUN
ajst-4909	74	13	the	the	DET
ajst-4909	74	14	algorithm	algorithm	NOUN
ajst-4909	74	15	steps	step	NOUN
ajst-4909	74	16	of	of	ADP
ajst-4909	74	17	the	the	DET
ajst-4909	74	18	k	k	NOUN
ajst-4909	74	19	-	-	PUNCT
ajst-4909	74	20	nearest	near	ADJ
ajst-4909	74	21	neighbor	neighbor	NOUN
ajst-4909	74	22	algorithm	algorithm	NOUN
ajst-4909	74	23	based	base	VERB
ajst-4909	74	24	on	on	ADP
ajst-4909	74	25	the	the	DET
ajst-4909	74	26	correlation	correlation	NOUN
ajst-4909	74	27	distance	distance	NOUN
ajst-4909	74	28	of	of	ADP
ajst-4909	74	29	attribute	attribute	NOUN
ajst-4909	74	30	values	value	NOUN
ajst-4909	74	31	are	be	AUX
ajst-4909	74	32	as	as	SCONJ
ajst-4909	74	33	follows	follow	VERB
ajst-4909	74	34	.	.	PUNCT
ajst-4909	75	1	118	118	NUM
ajst-4909	75	2	(	(	PUNCT
ajst-4909	75	3	1	1	NUM
ajst-4909	75	4	)	)	PUNCT
ajst-4909	75	5	calculate	calculate	VERB
ajst-4909	75	6	the	the	DET
ajst-4909	75	7	correlation	correlation	NOUN
ajst-4909	75	8	distance	distance	NOUN
ajst-4909	75	9	between	between	ADP
ajst-4909	75	10	the	the	DET
ajst-4909	75	11	training	training	NOUN
ajst-4909	75	12	sample	sample	NOUN
ajst-4909	75	13	and	and	CCONJ
ajst-4909	75	14	the	the	DET
ajst-4909	75	15	test	test	NOUN
ajst-4909	75	16	sample	sample	NOUN
ajst-4909	75	17	.	.	PUNCT
ajst-4909	76	1	(	(	PUNCT
ajst-4909	76	2	2	2	X
ajst-4909	76	3	)	)	PUNCT
ajst-4909	76	4	set	set	VERB
ajst-4909	76	5	a	a	DET
ajst-4909	76	6	suitable	suitable	ADJ
ajst-4909	76	7	k	k	PROPN
ajst-4909	76	8	value	value	NOUN
ajst-4909	76	9	,	,	PUNCT
ajst-4909	76	10	select	select	VERB
ajst-4909	76	11	the	the	DET
ajst-4909	76	12	k	k	PROPN
ajst-4909	76	13	training	training	NOUN
ajst-4909	76	14	samples	sample	NOUN
ajst-4909	76	15	with	with	ADP
ajst-4909	76	16	the	the	DET
ajst-4909	76	17	smallest	small	ADJ
ajst-4909	76	18	correlation	correlation	NOUN
ajst-4909	76	19	distance	distance	NOUN
ajst-4909	76	20	,	,	PUNCT
ajst-4909	76	21	and	and	CCONJ
ajst-4909	76	22	calculate	calculate	VERB
ajst-4909	76	23	the	the	DET
ajst-4909	76	24	number	number	NOUN
ajst-4909	76	25	of	of	ADP
ajst-4909	76	26	neighboring	neighboring	NOUN
ajst-4909	76	27	points	point	NOUN
ajst-4909	76	28	rn	rn	PROPN
ajst-4909	76	29	belonging	belong	VERB
ajst-4909	76	30	to	to	ADP
ajst-4909	76	31	different	different	ADJ
ajst-4909	76	32	classes	class	NOUN
ajst-4909	76	33	.	.	PUNCT
ajst-4909	77	1	samples	sample	NOUN
ajst-4909	77	2	,	,	PUNCT
ajst-4909	77	3	and	and	CCONJ
ajst-4909	77	4	calculate	calculate	VERB
ajst-4909	77	5	the	the	DET
ajst-4909	77	6	number	number	NOUN
ajst-4909	77	7	of	of	ADP
ajst-4909	77	8	neighboring	neighboring	NOUN
ajst-4909	77	9	points	point	NOUN
ajst-4909	77	10	rn	rn	PROPN
ajst-4909	77	11	belonging	belong	VERB
ajst-4909	77	12	to	to	ADP
ajst-4909	77	13	different	different	ADJ
ajst-4909	77	14	classes	class	NOUN
ajst-4909	77	15	.	.	PUNCT
ajst-4909	78	1	(	(	PUNCT
ajst-4909	78	2	3	3	X
ajst-4909	78	3	)	)	PUNCT
ajst-4909	78	4	calculate	calculate	NOUN
ajst-4909	78	5	the	the	DET
ajst-4909	78	6	class	class	NOUN
ajst-4909	78	7	confidence	confidence	NOUN
ajst-4909	78	8			NOUN
ajst-4909	78	9	,r	,r	VERB
ajst-4909	78	10	testt	testt	NOUN
ajst-4909	78	11	c	c	NOUN
ajst-4909	78	12	x	x	X
ajst-4909	78	13	of	of	ADP
ajst-4909	78	14	the	the	DET
ajst-4909	78	15	sample	sample	NOUN
ajst-4909	78	16	to	to	PART
ajst-4909	78	17	be	be	AUX
ajst-4909	78	18	classified	classify	VERB
ajst-4909	78	19	and	and	CCONJ
ajst-4909	78	20	each	each	DET
ajst-4909	78	21	class	class	NOUN
ajst-4909	78	22			PROPN
ajst-4909	78	23	,r	,r	NOUN
ajst-4909	78	24	testt	testt	NOUN
ajst-4909	78	25	c	c	NOUN
ajst-4909	78	26	x	x	X
ajst-4909	78	27	,	,	PUNCT
ajst-4909	78	28	and	and	CCONJ
ajst-4909	78	29	the	the	DET
ajst-4909	78	30	class	class	NOUN
ajst-4909	78	31	of	of	ADP
ajst-4909	78	32	the	the	DET
ajst-4909	78	33	sample	sample	NOUN
ajst-4909	78	34	to	to	PART
ajst-4909	78	35	be	be	AUX
ajst-4909	78	36	classified	classify	VERB
ajst-4909	78	37	is	be	AUX
ajst-4909	78	38	determined	determine	VERB
ajst-4909	78	39	as	as	ADP
ajst-4909	78	40	the	the	DET
ajst-4909	78	41	class	class	NOUN
ajst-4909	78	42	with	with	ADP
ajst-4909	78	43	the	the	DET
ajst-4909	78	44	smallest	small	ADJ
ajst-4909	78	45	class	class	NOUN
ajst-4909	78	46	confidence	confidence	NOUN
ajst-4909	78	47	.	.	PUNCT
ajst-4909	79	1	3	3	X
ajst-4909	79	2	.	.	X
ajst-4909	79	3	experimental	experimental	ADJ
ajst-4909	79	4	procedure	procedure	NOUN
ajst-4909	79	5	and	and	CCONJ
ajst-4909	79	6	conclusions	conclusion	NOUN
ajst-4909	79	7	3.1	3.1	NUM
ajst-4909	79	8	.	.	PUNCT
ajst-4909	80	1	data	datum	NOUN
ajst-4909	80	2	reduction	reduction	NOUN
ajst-4909	80	3	a	a	DET
ajst-4909	80	4	total	total	NOUN
ajst-4909	80	5	of	of	ADP
ajst-4909	80	6	1000	1000	NUM
ajst-4909	80	7	carbon	carbon	NOUN
ajst-4909	80	8	star	star	NOUN
ajst-4909	80	9	spectral	spectral	PROPN
ajst-4909	80	10	data	datum	NOUN
ajst-4909	80	11	obtained	obtain	VERB
ajst-4909	80	12	using	use	VERB
ajst-4909	80	13	lamost	lamost	ADJ
ajst-4909	80	14	dr4	dr4	NOUN
ajst-4909	80	15	and	and	CCONJ
ajst-4909	80	16	lamost	lamost	VERB
ajst-4909	80	17	dr7	dr7	NOUN
ajst-4909	80	18	crossover	crossover	ADP
ajst-4909	80	19	data	datum	NOUN
ajst-4909	80	20	were	be	AUX
ajst-4909	80	21	used	use	VERB
ajst-4909	80	22	for	for	ADP
ajst-4909	80	23	the	the	DET
ajst-4909	80	24	experimental	experimental	ADJ
ajst-4909	80	25	data	datum	NOUN
ajst-4909	80	26	.	.	PUNCT
ajst-4909	81	1	after	after	ADP
ajst-4909	81	2	normalization	normalization	NOUN
ajst-4909	81	3	,	,	PUNCT
ajst-4909	81	4	the	the	DET
ajst-4909	81	5	data	datum	NOUN
ajst-4909	81	6	were	be	AUX
ajst-4909	81	7	downscaled	downscale	VERB
ajst-4909	81	8	to	to	ADP
ajst-4909	81	9	two	two	NUM
ajst-4909	81	10	dimensions	dimension	NOUN
ajst-4909	81	11	using	use	VERB
ajst-4909	81	12	pca	pca	PROPN
ajst-4909	81	13	and	and	CCONJ
ajst-4909	81	14	t	t	PROPN
ajst-4909	81	15	-	-	PUNCT
ajst-4909	81	16	sne	sne	PROPN
ajst-4909	81	17	algorithms	algorithm	NOUN
ajst-4909	81	18	.	.	PUNCT
ajst-4909	82	1	table	table	NOUN
ajst-4909	82	2	1	1	NUM
ajst-4909	82	3	.	.	PUNCT
ajst-4909	82	4	carbon	carbon	NOUN
ajst-4909	82	5	star	star	NOUN
ajst-4909	82	6	type	type	NOUN
ajst-4909	82	7	sample	sample	NOUN
ajst-4909	82	8	data	datum	NOUN
ajst-4909	82	9	type	type	NOUN
ajst-4909	82	10	ba	ba	NOUN
ajst-4909	83	1	c	c	NOUN
ajst-4909	83	2	-	-	NOUN
ajst-4909	83	3	h	h	NOUN
ajst-4909	83	4	c	c	NOUN
ajst-4909	83	5	-	-	PUNCT
ajst-4909	83	6	j	j	PROPN
ajst-4909	83	7	c	c	PROPN
ajst-4909	83	8	-	-	PUNCT
ajst-4909	83	9	n	n	PRON
ajst-4909	83	10	c	c	NOUN
ajst-4909	83	11	-	-	PUNCT
ajst-4909	83	12	r	r	NOUN
ajst-4909	83	13	lamost	lamost	NOUN
ajst-4909	83	14	dr4	dr4	NOUN
ajst-4909	83	15	719	719	NUM
ajst-4909	83	16	864	864	NUM
ajst-4909	83	17	400	400	NUM
ajst-4909	83	18	266	266	NUM
ajst-4909	83	19	226	226	NUM
ajst-4909	83	20	lamost	lamost	ADJ
ajst-4909	83	21	dr7	dr7	NOUN
ajst-4909	83	22	669	669	NUM
ajst-4909	83	23	817	817	NUM
ajst-4909	83	24	297	297	NUM
ajst-4909	83	25	216	216	NUM
ajst-4909	83	26	212	212	NUM
ajst-4909	83	27	the	the	DET
ajst-4909	83	28	distribution	distribution	NOUN
ajst-4909	83	29	of	of	ADP
ajst-4909	83	30	the	the	DET
ajst-4909	83	31	data	datum	NOUN
ajst-4909	83	32	in	in	ADP
ajst-4909	83	33	the	the	DET
ajst-4909	83	34	two	two	NUM
ajst-4909	83	35	-	-	PUNCT
ajst-4909	83	36	dimensional	dimensional	ADJ
ajst-4909	83	37	plane	plane	NOUN
ajst-4909	83	38	after	after	SCONJ
ajst-4909	83	39	pca	pca	NOUN
ajst-4909	83	40	downscaling	downscaling	NOUN
ajst-4909	83	41	is	be	AUX
ajst-4909	83	42	shown	show	VERB
ajst-4909	83	43	in	in	ADP
ajst-4909	83	44	figure	figure	NOUN
ajst-4909	83	45	1	1	NUM
ajst-4909	83	46	.	.	PUNCT
ajst-4909	83	47	figure	figure	NOUN
ajst-4909	83	48	1	1	NUM
ajst-4909	83	49	.	.	PUNCT
ajst-4909	83	50	distribution	distribution	NOUN
ajst-4909	83	51	of	of	ADP
ajst-4909	83	52	data	datum	NOUN
ajst-4909	83	53	after	after	ADP
ajst-4909	83	54	dimension	dimension	NOUN
ajst-4909	83	55	reduction	reduction	NOUN
ajst-4909	83	56	by	by	ADP
ajst-4909	83	57	pca	pca	NOUN
ajst-4909	83	58	figure	figure	NOUN
ajst-4909	83	59	2	2	NUM
ajst-4909	83	60	.	.	PUNCT
ajst-4909	83	61	distribution	distribution	NOUN
ajst-4909	83	62	of	of	ADP
ajst-4909	83	63	data	datum	NOUN
ajst-4909	83	64	after	after	ADP
ajst-4909	83	65	dimension	dimension	NOUN
ajst-4909	83	66	reduction	reduction	NOUN
ajst-4909	83	67	by	by	ADP
ajst-4909	83	68	pca	pca	NOUN
ajst-4909	83	69	figure	figure	NOUN
ajst-4909	83	70	3	3	NUM
ajst-4909	83	71	shows	show	VERB
ajst-4909	83	72	the	the	DET
ajst-4909	83	73	distribution	distribution	NOUN
ajst-4909	83	74	of	of	ADP
ajst-4909	83	75	the	the	DET
ajst-4909	83	76	data	datum	NOUN
ajst-4909	83	77	after	after	SCONJ
ajst-4909	83	78	t	t	PROPN
ajst-4909	83	79	sne	sne	PROPN
ajst-4909	83	80	is	be	AUX
ajst-4909	83	81	reduced	reduce	VERB
ajst-4909	83	82	to	to	ADP
ajst-4909	83	83	two	two	NUM
ajst-4909	83	84	dimensions	dimension	NOUN
ajst-4909	83	85	.	.	PUNCT
ajst-4909	84	1	since	since	SCONJ
ajst-4909	84	2	t	t	PROPN
ajst-4909	84	3	sne	sne	PROPN
ajst-4909	84	4	is	be	AUX
ajst-4909	84	5	based	base	VERB
ajst-4909	84	6	on	on	ADP
ajst-4909	84	7	stream	stream	NOUN
ajst-4909	84	8	shape	shape	NOUN
ajst-4909	84	9	learning	learning	NOUN
ajst-4909	84	10	,	,	PUNCT
ajst-4909	84	11	the	the	DET
ajst-4909	84	12	data	datum	NOUN
ajst-4909	84	13	after	after	ADP
ajst-4909	84	14	dimensionality	dimensionality	NOUN
ajst-4909	84	15	reduction	reduction	NOUN
ajst-4909	84	16	is	be	AUX
ajst-4909	84	17	divided	divide	VERB
ajst-4909	84	18	into	into	ADP
ajst-4909	84	19	different	different	ADJ
ajst-4909	84	20	stream	stream	NOUN
ajst-4909	84	21	shapes	shape	NOUN
ajst-4909	84	22	when	when	SCONJ
ajst-4909	84	23	in	in	ADP
ajst-4909	84	24	contrast	contrast	NOUN
ajst-4909	84	25	,	,	PUNCT
ajst-4909	84	26	the	the	DET
ajst-4909	84	27	data	datum	NOUN
ajst-4909	84	28	after	after	ADP
ajst-4909	84	29	dimensionality	dimensionality	NOUN
ajst-4909	84	30	reduction	reduction	NOUN
ajst-4909	84	31	by	by	ADP
ajst-4909	84	32	pca	pca	PROPN
ajst-4909	84	33	are	be	AUX
ajst-4909	84	34	basically	basically	ADV
ajst-4909	84	35	in	in	ADP
ajst-4909	84	36	the	the	DET
ajst-4909	84	37	form	form	NOUN
ajst-4909	84	38	of	of	ADP
ajst-4909	84	39	blocks	block	NOUN
ajst-4909	84	40	and	and	CCONJ
ajst-4909	84	41	lines	line	NOUN
ajst-4909	84	42	.	.	PUNCT
ajst-4909	85	1	more	more	ADV
ajst-4909	85	2	importantly	importantly	ADV
ajst-4909	85	3	,	,	PUNCT
ajst-4909	85	4	the	the	DET
ajst-4909	85	5	except	except	SCONJ
ajst-4909	85	6	for	for	ADP
ajst-4909	85	7	a	a	DET
ajst-4909	85	8	very	very	ADV
ajst-4909	85	9	small	small	ADJ
ajst-4909	85	10	amount	amount	NOUN
ajst-4909	85	11	of	of	ADP
ajst-4909	85	12	data	datum	NOUN
ajst-4909	85	13	,	,	PUNCT
ajst-4909	85	14	all	all	DET
ajst-4909	85	15	data	datum	NOUN
ajst-4909	85	16	of	of	ADP
ajst-4909	85	17	the	the	DET
ajst-4909	85	18	same	same	ADJ
ajst-4909	85	19	type	type	NOUN
ajst-4909	85	20	can	can	AUX
ajst-4909	85	21	be	be	AUX
ajst-4909	85	22	aggregated	aggregate	VERB
ajst-4909	85	23	in	in	ADP
ajst-4909	85	24	one	one	NUM
ajst-4909	85	25	region	region	NOUN
ajst-4909	85	26	,	,	PUNCT
ajst-4909	85	27	and	and	CCONJ
ajst-4909	85	28	there	there	PRON
ajst-4909	85	29	are	be	VERB
ajst-4909	85	30	obvious	obvious	ADJ
ajst-4909	85	31	classification	classification	NOUN
ajst-4909	85	32	boundaries	boundary	NOUN
ajst-4909	85	33	between	between	ADP
ajst-4909	85	34	different	different	ADJ
ajst-4909	85	35	categories	category	NOUN
ajst-4909	85	36	of	of	ADP
ajst-4909	85	37	data	data	PROPN
ajst-4909	85	38	.	.	PUNCT
ajst-4909	86	1	figure	figure	VERB
ajst-4909	86	2	3	3	NUM
ajst-4909	86	3	.	.	PUNCT
ajst-4909	87	1	distribution	distribution	NOUN
ajst-4909	87	2	of	of	ADP
ajst-4909	87	3	data	datum	NOUN
ajst-4909	87	4	after	after	ADP
ajst-4909	87	5	dimension	dimension	NOUN
ajst-4909	87	6	reduction	reduction	NOUN
ajst-4909	87	7	by	by	ADP
ajst-4909	87	8	t	t	PROPN
ajst-4909	87	9	-	-	PUNCT
ajst-4909	87	10	sne	sne	PROPN
ajst-4909	87	11	comparing	compare	VERB
ajst-4909	87	12	figure	figure	NOUN
ajst-4909	87	13	1	1	NUM
ajst-4909	87	14	and	and	CCONJ
ajst-4909	87	15	figure	figure	NOUN
ajst-4909	87	16	2	2	NUM
ajst-4909	87	17	,	,	PUNCT
ajst-4909	87	18	it	it	PRON
ajst-4909	87	19	can	can	AUX
ajst-4909	87	20	be	be	AUX
ajst-4909	87	21	seen	see	VERB
ajst-4909	87	22	that	that	SCONJ
ajst-4909	87	23	when	when	SCONJ
ajst-4909	87	24	reducing	reduce	VERB
ajst-4909	87	25	the	the	DET
ajst-4909	87	26	data	datum	NOUN
ajst-4909	87	27	to	to	ADP
ajst-4909	87	28	two	two	NUM
ajst-4909	87	29	dimensions	dimension	NOUN
ajst-4909	87	30	,	,	PUNCT
ajst-4909	87	31	t	t	PROPN
ajst-4909	87	32	sne	sne	PROPN
ajst-4909	87	33	is	be	AUX
ajst-4909	87	34	able	able	ADJ
ajst-4909	87	35	to	to	PART
ajst-4909	87	36	produce	produce	VERB
ajst-4909	87	37	more	more	ADJ
ajst-4909	87	38	robust	robust	ADJ
ajst-4909	87	39	classification	classification	NOUN
ajst-4909	87	40	boundaries	boundary	NOUN
ajst-4909	87	41	,	,	PUNCT
ajst-4909	87	42	which	which	PRON
ajst-4909	87	43	provides	provide	VERB
ajst-4909	87	44	better	well	ADJ
ajst-4909	87	45	conditions	condition	NOUN
ajst-4909	87	46	for	for	ADP
ajst-4909	87	47	classification	classification	NOUN
ajst-4909	87	48	.	.	PUNCT
ajst-4909	88	1	conditions	condition	NOUN
ajst-4909	88	2	.	.	PUNCT
ajst-4909	89	1	3.2	3.2	NUM
ajst-4909	89	2	.	.	PUNCT
ajst-4909	89	3	analysis	analysis	NOUN
ajst-4909	89	4	of	of	ADP
ajst-4909	89	5	results	result	NOUN
ajst-4909	89	6	the	the	DET
ajst-4909	89	7	idea	idea	NOUN
ajst-4909	89	8	of	of	ADP
ajst-4909	89	9	the	the	DET
ajst-4909	89	10	traditional	traditional	ADJ
ajst-4909	89	11	dimensionality	dimensionality	NOUN
ajst-4909	89	12	reduction	reduction	NOUN
ajst-4909	89	13	algorithm	algorithm	NOUN
ajst-4909	89	14	pca	pca	PROPN
ajst-4909	89	15	is	be	AUX
ajst-4909	89	16	to	to	PART
ajst-4909	89	17	make	make	VERB
ajst-4909	89	18	the	the	DET
ajst-4909	89	19	data	datum	NOUN
ajst-4909	89	20	retain	retain	VERB
ajst-4909	89	21	the	the	DET
ajst-4909	89	22	maximum	maximum	ADJ
ajst-4909	89	23	variance	variance	NOUN
ajst-4909	89	24	after	after	ADP
ajst-4909	89	25	the	the	DET
ajst-4909	89	26	dimensionality	dimensionality	NOUN
ajst-4909	89	27	reduction	reduction	NOUN
ajst-4909	89	28	.	.	PUNCT
ajst-4909	90	1	the	the	DET
ajst-4909	90	2	dimensionality	dimensionality	NOUN
ajst-4909	90	3	reduction	reduction	NOUN
ajst-4909	90	4	process	process	NOUN
ajst-4909	90	5	of	of	ADP
ajst-4909	90	6	pca	pca	PROPN
ajst-4909	90	7	for	for	ADP
ajst-4909	90	8	spectral	spectral	ADJ
ajst-4909	90	9	data	datum	NOUN
ajst-4909	90	10	is	be	AUX
ajst-4909	90	11	prone	prone	ADJ
ajst-4909	90	12	to	to	AUX
ajst-4909	90	13	data	datum	NOUN
ajst-4909	90	14	the	the	DET
ajst-4909	90	15	problem	problem	NOUN
ajst-4909	90	16	of	of	ADP
ajst-4909	90	17	data	datum	NOUN
ajst-4909	90	18	overlap	overlap	NOUN
ajst-4909	90	19	arises	arise	VERB
ajst-4909	90	20	.	.	PUNCT
ajst-4909	91	1	the	the	DET
ajst-4909	91	2	problem	problem	NOUN
ajst-4909	91	3	is	be	AUX
ajst-4909	91	4	well	well	ADV
ajst-4909	91	5	solved	solve	VERB
ajst-4909	91	6	in	in	ADP
ajst-4909	91	7	the	the	DET
ajst-4909	91	8	t	t	PROPN
ajst-4909	91	9	sne	sne	NOUN
ajst-4909	91	10	algorithm	algorithm	PROPN
ajst-4909	91	11	.	.	PUNCT
ajst-4909	92	1	according	accord	VERB
ajst-4909	92	2	to	to	ADP
ajst-4909	92	3	the	the	DET
ajst-4909	92	4	flow	flow	NOUN
ajst-4909	92	5	shape	shape	NOUN
ajst-4909	92	6	learning	learn	VERB
ajst-4909	92	7	algorithm	algorithm	PROPN
ajst-4909	92	8	's	's	PART
ajst-4909	92	9	basic	basic	ADJ
ajst-4909	92	10	idea	idea	NOUN
ajst-4909	92	11	,	,	PUNCT
ajst-4909	92	12	t	t	PROPN
ajst-4909	92	13	sne	sne	PROPN
ajst-4909	92	14	can	can	AUX
ajst-4909	92	15	effectively	effectively	ADV
ajst-4909	92	16	extract	extract	VERB
ajst-4909	92	17	the	the	DET
ajst-4909	92	18	stream	stream	NOUN
ajst-4909	92	19	shape	shape	NOUN
ajst-4909	92	20	structure	structure	NOUN
ajst-4909	92	21	in	in	ADP
ajst-4909	92	22	the	the	DET
ajst-4909	92	23	high	high	ADV
ajst-4909	92	24	-	-	PUNCT
ajst-4909	92	25	dimensional	dimensional	ADJ
ajst-4909	92	26	space	space	NOUN
ajst-4909	92	27	the	the	DET
ajst-4909	92	28	t	t	PROPN
ajst-4909	92	29	sne	sne	NOUN
ajst-4909	92	30	can	can	AUX
ajst-4909	92	31	effectively	effectively	ADV
ajst-4909	92	32	extract	extract	VERB
ajst-4909	92	33	the	the	DET
ajst-4909	92	34	stream	stream	NOUN
ajst-4909	92	35	structure	structure	NOUN
ajst-4909	92	36	in	in	ADP
ajst-4909	92	37	the	the	DET
ajst-4909	92	38	high	high	ADJ
ajst-4909	92	39	-	-	PUNCT
ajst-4909	92	40	dimensional	dimensional	ADJ
ajst-4909	92	41	space	space	NOUN
ajst-4909	92	42	,	,	PUNCT
ajst-4909	92	43	thus	thus	ADV
ajst-4909	92	44	avoiding	avoid	VERB
ajst-4909	92	45	the	the	DET
ajst-4909	92	46	overlap	overlap	NOUN
ajst-4909	92	47	of	of	ADP
ajst-4909	92	48	data	datum	NOUN
ajst-4909	92	49	in	in	ADP
ajst-4909	92	50	the	the	DET
ajst-4909	92	51	low	low	ADJ
ajst-4909	92	52	-	-	PUNCT
ajst-4909	92	53	dimensional	dimensional	ADJ
ajst-4909	92	54	space	space	NOUN
ajst-4909	92	55	.	.	PUNCT
ajst-4909	93	1	4	4	X
ajst-4909	93	2	.	.	X
ajst-4909	93	3	conclusion	conclusion	NOUN
ajst-4909	93	4	carbon	carbon	NOUN
ajst-4909	93	5	stars	star	NOUN
ajst-4909	93	6	are	be	AUX
ajst-4909	93	7	a	a	DET
ajst-4909	93	8	rare	rare	ADJ
ajst-4909	93	9	type	type	NOUN
ajst-4909	93	10	of	of	ADP
ajst-4909	93	11	star	star	NOUN
ajst-4909	93	12	that	that	PRON
ajst-4909	93	13	is	be	AUX
ajst-4909	93	14	an	an	DET
ajst-4909	93	15	important	important	ADJ
ajst-4909	93	16	part	part	NOUN
ajst-4909	93	17	of	of	ADP
ajst-4909	93	18	astronomical	astronomical	ADJ
ajst-4909	93	19	data	datum	NOUN
ajst-4909	93	20	processing	processing	NOUN
ajst-4909	93	21	.	.	PUNCT
ajst-4909	94	1	this	this	DET
ajst-4909	94	2	paper	paper	NOUN
ajst-4909	94	3	analyzes	analyze	VERB
ajst-4909	94	4	the	the	DET
ajst-4909	94	5	distribution	distribution	NOUN
ajst-4909	94	6	of	of	ADP
ajst-4909	94	7	spectral	spectral	ADJ
ajst-4909	94	8	data	datum	NOUN
ajst-4909	94	9	in	in	ADP
ajst-4909	94	10	high	high	ADJ
ajst-4909	94	11	-	-	PUNCT
ajst-4909	94	12	dimensional	dimensional	ADJ
ajst-4909	94	13	space	space	NOUN
ajst-4909	94	14	.	.	PUNCT
ajst-4909	95	1	in	in	ADP
ajst-4909	95	2	this	this	DET
ajst-4909	95	3	paper	paper	NOUN
ajst-4909	95	4	,	,	PUNCT
ajst-4909	95	5	we	we	PRON
ajst-4909	95	6	analyze	analyze	VERB
ajst-4909	95	7	the	the	DET
ajst-4909	95	8	distribution	distribution	NOUN
ajst-4909	95	9	of	of	ADP
ajst-4909	95	10	spectral	spectral	ADJ
ajst-4909	95	11	data	datum	NOUN
ajst-4909	95	12	in	in	ADP
ajst-4909	95	13	highdimensional	highdimensional	ADJ
ajst-4909	95	14	space	space	NOUN
ajst-4909	95	15	,	,	PUNCT
ajst-4909	95	16	and	and	CCONJ
ajst-4909	95	17	use	use	VERB
ajst-4909	95	18	the	the	DET
ajst-4909	95	19	t	t	PROPN
ajst-4909	95	20	-	-	PUNCT
ajst-4909	95	21	sne	sne	NOUN
ajst-4909	95	22	method	method	NOUN
ajst-4909	95	23	in	in	ADP
ajst-4909	95	24	stream	stream	NOUN
ajst-4909	95	25	learning	learn	VERB
ajst-4909	95	26	to	to	PART
ajst-4909	95	27	reduce	reduce	VERB
ajst-4909	95	28	the	the	DET
ajst-4909	95	29	dimensionality	dimensionality	NOUN
ajst-4909	95	30	of	of	ADP
ajst-4909	95	31	lamost	lamost	ADJ
ajst-4909	95	32	carbon	carbon	NOUN
ajst-4909	95	33	spectral	spectral	ADJ
ajst-4909	95	34	data	datum	NOUN
ajst-4909	95	35	.	.	PUNCT
ajst-4909	96	1	the	the	DET
ajst-4909	96	2	experimental	experimental	ADJ
ajst-4909	96	3	results	result	NOUN
ajst-4909	96	4	show	show	VERB
ajst-4909	96	5	that	that	SCONJ
ajst-4909	96	6	the	the	DET
ajst-4909	96	7	t	t	PROPN
ajst-4909	96	8	-	-	PUNCT
ajst-4909	96	9	sne	sne	NOUN
ajst-4909	96	10	method	method	NOUN
ajst-4909	96	11	can	can	AUX
ajst-4909	96	12	reduce	reduce	VERB
ajst-4909	96	13	the	the	DET
ajst-4909	96	14	dimensionality	dimensionality	NOUN
ajst-4909	96	15	of	of	ADP
ajst-4909	96	16	low	low	ADJ
ajst-4909	96	17	signal	signal	NOUN
ajst-4909	96	18	-	-	PUNCT
ajst-4909	96	19	to	to	ADP
ajst-4909	96	20	-	-	PUNCT
ajst-4909	96	21	noise	noise	NOUN
ajst-4909	96	22	ratio	ratio	NOUN
ajst-4909	96	23	stellar	stellar	ADJ
ajst-4909	96	24	spectral	spectral	ADJ
ajst-4909	96	25	data	datum	NOUN
ajst-4909	96	26	more	more	ADV
ajst-4909	96	27	effectively	effectively	ADV
ajst-4909	96	28	than	than	ADP
ajst-4909	96	29	the	the	DET
ajst-4909	96	30	traditional	traditional	ADJ
ajst-4909	96	31	pca	pca	NOUN
ajst-4909	96	32	method	method	NOUN
ajst-4909	96	33	.	.	PUNCT
ajst-4909	97	1	the	the	DET
ajst-4909	97	2	algorithm	algorithm	NOUN
ajst-4909	97	3	used	use	VERB
ajst-4909	97	4	can	can	AUX
ajst-4909	97	5	significantly	significantly	ADV
ajst-4909	97	6	reduce	reduce	VERB
ajst-4909	97	7	the	the	DET
ajst-4909	97	8	workload	workload	NOUN
ajst-4909	97	9	of	of	ADP
ajst-4909	97	10	astronomers	astronomer	NOUN
ajst-4909	97	11	and	and	CCONJ
ajst-4909	97	12	has	have	VERB
ajst-4909	97	13	a	a	DET
ajst-4909	97	14	certain	certain	ADJ
ajst-4909	97	15	degree	degree	NOUN
ajst-4909	97	16	of	of	ADP
ajst-4909	97	17	the	the	DET
ajst-4909	97	18	algorithm	algorithm	NOUN
ajst-4909	97	19	used	use	VERB
ajst-4909	97	20	can	can	AUX
ajst-4909	97	21	significantly	significantly	ADV
ajst-4909	97	22	reduce	reduce	VERB
ajst-4909	97	23	the	the	DET
ajst-4909	97	24	workload	workload	NOUN
ajst-4909	97	25	of	of	ADP
ajst-4909	97	26	astronomers	astronomer	NOUN
ajst-4909	97	27	and	and	CCONJ
ajst-4909	97	28	has	have	VERB
ajst-4909	97	29	some	some	DET
ajst-4909	97	30	application	application	NOUN
ajst-4909	97	31	value	value	NOUN
ajst-4909	97	32	.	.	PUNCT
ajst-4909	98	1	119	119	NUM
ajst-4909	98	2	references	reference	NOUN
ajst-4909	98	3	[	[	X
ajst-4909	98	4	1	1	NUM
ajst-4909	98	5	]	]	X
ajst-4909	98	6	navarro	navarro	PROPN
ajst-4909	98	7	s	s	PART
ajst-4909	98	8	g	g	NOUN
ajst-4909	98	9	,	,	PUNCT
ajst-4909	98	10	corradi	corradi	NOUN
ajst-4909	98	11	r	r	NOUN
ajst-4909	98	12	l	l	NOUN
ajst-4909	98	13	m	m	NOUN
ajst-4909	98	14	,	,	PUNCT
ajst-4909	98	15	mampaso	mampaso	VERB
ajst-4909	99	1	a	a	PRON
ajst-4909	99	2	.	.	PUNCT
ajst-4909	100	1	automatic	automatic	ADJ
ajst-4909	100	2	spectral	spectral	ADJ
ajst-4909	100	3	classification	classification	NOUN
ajst-4909	100	4	of	of	ADP
ajst-4909	100	5	stellar	stellar	ADJ
ajst-4909	100	6	spectra	spectra	NOUN
ajst-4909	100	7	with	with	ADP
ajst-4909	100	8	low	low	ADJ
ajst-4909	100	9	signal	signal	NOUN
ajst-4909	100	10	-	-	PUNCT
ajst-4909	100	11	to	to	ADP
ajst-4909	100	12	-	-	PUNCT
ajst-4909	100	13	noise	noise	NOUN
ajst-4909	100	14	ratio	ratio	NOUN
ajst-4909	100	15	using	use	VERB
ajst-4909	100	16	artificial	artificial	ADJ
ajst-4909	100	17	neural	neural	ADJ
ajst-4909	100	18	networks[j	networks[j	PROPN
ajst-4909	100	19	]	]	X
ajst-4909	100	20	.	.	PUNCT
ajst-4909	101	1	astronomy	astronomy	NOUN
ajst-4909	101	2	and	and	CCONJ
ajst-4909	101	3	astrophysics	astrophysic	NOUN
ajst-4909	101	4	,	,	PUNCT
ajst-4909	101	5	2012	2012	NUM
ajst-4909	101	6	,	,	PUNCT
ajst-4909	101	7	538:76	538:76	NUM
ajst-4909	101	8	.	.	PUNCT
ajst-4909	102	1	[	[	X
ajst-4909	102	2	2	2	NUM
ajst-4909	102	3	]	]	X
ajst-4909	102	4	kheirdastan	kheirdastan	PROPN
ajst-4909	102	5	s	s	PROPN
ajst-4909	102	6	b	b	PROPN
ajst-4909	102	7	m	m	PROPN
ajst-4909	102	8	.	.	PUNCT
ajst-4909	102	9	sdss	sdss	PROPN
ajst-4909	102	10	-	-	PUNCT
ajst-4909	102	11	dr12	dr12	PROPN
ajst-4909	102	12	bulk	bulk	ADJ
ajst-4909	102	13	stellar	stellar	ADJ
ajst-4909	102	14	spectral	spectral	ADJ
ajst-4909	102	15	classification	classification	NOUN
ajst-4909	102	16	:	:	PUNCT
ajst-4909	102	17	artificial	artificial	ADJ
ajst-4909	102	18	neural	neural	ADJ
ajst-4909	102	19	networks	network	NOUN
ajst-4909	102	20	approach[j	approach[j	PROPN
ajst-4909	102	21	]	]	PUNCT
ajst-4909	102	22	.	.	PUNCT
ajst-4909	103	1	astrophysics	astrophysic	NOUN
ajst-4909	103	2	and	and	CCONJ
ajst-4909	103	3	space	space	NOUN
ajst-4909	103	4	science	science	NOUN
ajst-4909	103	5	,	,	PUNCT
ajst-4909	103	6	2016	2016	NUM
ajst-4909	103	7	,	,	PUNCT
ajst-4909	103	8	361(9	361(9	NUM
ajst-4909	103	9	)	)	PUNCT
ajst-4909	103	10	.	.	PUNCT
ajst-4909	104	1	[	[	X
ajst-4909	104	2	3	3	X
ajst-4909	104	3	]	]	AUX
ajst-4909	104	4	bulanov	bulanov	NOUN
ajst-4909	104	5	a	a	DET
ajst-4909	104	6	v	v	NOUN
ajst-4909	104	7	.	.	PUNCT
ajst-4909	105	1	using	use	VERB
ajst-4909	105	2	of	of	ADP
ajst-4909	105	3	ultrasound	ultrasound	NOUN
ajst-4909	105	4	in	in	ADP
ajst-4909	105	5	automated	automate	VERB
ajst-4909	105	6	laser	laser	NOUN
ajst-4909	105	7	induced	induce	VERB
ajst-4909	105	8	breakdown	breakdown	NOUN
ajst-4909	105	9	spectroscopy	spectroscopy	NOUN
ajst-4909	105	10	complex	complex	ADJ
ajst-4909	105	11	for	for	ADP
ajst-4909	105	12	operational	operational	ADJ
ajst-4909	105	13	study	study	NOUN
ajst-4909	105	14	of	of	ADP
ajst-4909	105	15	spectral	spectral	ADJ
ajst-4909	105	16	characteristics	characteristic	NOUN
ajst-4909	105	17	of	of	ADP
ajst-4909	105	18	seawater	seawater	NOUN
ajst-4909	105	19	of	of	ADP
ajst-4909	105	20	carbon	carbon	NOUN
ajst-4909	105	21	polygons[j	polygons[j	PROPN
ajst-4909	105	22	]	]	PUNCT
ajst-4909	105	23	.	.	PUNCT
ajst-4909	106	1	bulletin	bulletin	NOUN
ajst-4909	106	2	of	of	ADP
ajst-4909	106	3	the	the	DET
ajst-4909	106	4	russian	russian	PROPN
ajst-4909	106	5	academy	academy	PROPN
ajst-4909	106	6	of	of	ADP
ajst-4909	106	7	sciences	sciences	PROPN
ajst-4909	106	8	:	:	PUNCT
ajst-4909	106	9	physics	physics	NOUN
ajst-4909	106	10	,	,	PUNCT
ajst-4909	106	11	2022	2022	NUM
ajst-4909	106	12	,	,	PUNCT
ajst-4909	106	13	86(1):s32	86(1):s32	NUM
ajst-4909	106	14	-	-	PUNCT
ajst-4909	106	15	s36	s36	NOUN
ajst-4909	106	16	.	.	PUNCT
ajst-4909	107	1	[	[	X
ajst-4909	107	2	4	4	X
ajst-4909	107	3	]	]	X
ajst-4909	107	4	fuqiang	fuqiang	PROPN
ajst-4909	107	5	c	c	PROPN
ajst-4909	107	6	,	,	PUNCT
ajst-4909	107	7	yan	yan	PROPN
ajst-4909	107	8	w	w	PROPN
ajst-4909	107	9	,	,	PUNCT
ajst-4909	107	10	yude	yude	PROPN
ajst-4909	107	11	b	b	PROPN
ajst-4909	107	12	,	,	PUNCT
ajst-4909	107	13	et	et	PROPN
ajst-4909	107	14	al	al	PROPN
ajst-4909	107	15	.	.	PUNCT
ajst-4909	107	16	spectral	spectral	ADJ
ajst-4909	107	17	classification	classification	NOUN
ajst-4909	107	18	using	use	VERB
ajst-4909	107	19	restricted	restrict	VERB
ajst-4909	107	20	boltzmann	boltzmann	PROPN
ajst-4909	107	21	machine[j	machine[j	PROPN
ajst-4909	107	22	]	]	PUNCT
ajst-4909	107	23	.	.	PUNCT
ajst-4909	108	1	publications	publication	NOUN
ajst-4909	108	2	of	of	ADP
ajst-4909	108	3	the	the	DET
ajst-4909	108	4	astronomical	astronomical	ADJ
ajst-4909	108	5	society	society	NOUN
ajst-4909	108	6	of	of	ADP
ajst-4909	108	7	australia	australia	PROPN
ajst-4909	108	8	,	,	PUNCT
ajst-4909	108	9	2014	2014	NUM
ajst-4909	108	10	,	,	PUNCT
ajst-4909	108	11	31:386	31:386	NUM
ajst-4909	108	12	-	-	SYM
ajst-4909	108	13	406	406	NUM
ajst-4909	108	14	.	.	PUNCT
ajst-4909	109	1	[	[	X
ajst-4909	109	2	5	5	NUM
ajst-4909	109	3	]	]	X
ajst-4909	109	4	a	a	PRON
ajst-4909	109	5	.	.	PUNCT
ajst-4909	110	1	schreiben	schreiben	PROPN
ajst-4909	110	2	des	des	PROPN
ajst-4909	110	3	herrn	herrn	PROPN
ajst-4909	110	4	professors	professors	PROPN
ajst-4909	110	5	secchi	secchi	PROPN
ajst-4909	110	6	an	an	DET
ajst-4909	110	7	den	den	NOUN
ajst-4909	110	8	herausgeber[j	herausgeber[j	NOUN
ajst-4909	110	9	]	]	PUNCT
ajst-4909	110	10	.	.	PUNCT
ajst-4909	111	1	astronomische	astronomische	PROPN
ajst-4909	111	2	nachrichten	nachrichten	PROPN
ajst-4909	111	3	,	,	PUNCT
ajst-4909	111	4	1869	1869	NUM
ajst-4909	111	5	.	.	PUNCT
ajst-4909	112	1	[	[	X
ajst-4909	112	2	6	6	NUM
ajst-4909	112	3	]	]	SYM
ajst-4909	112	4	t	t	PROPN
ajst-4909	112	5	,	,	PUNCT
ajst-4909	112	6	lloyd	lloyd	PROPN
ajst-4909	112	7	,	,	PUNCT
ajst-4909	112	8	evans	evans	PROPN
ajst-4909	112	9	.	.	PUNCT
ajst-4909	113	1	carbon	carbon	PROPN
ajst-4909	113	2	stars[j	stars[j	PROPN
ajst-4909	113	3	]	]	PUNCT
ajst-4909	113	4	.	.	PUNCT
ajst-4909	114	1	journal	journal	PROPN
ajst-4909	114	2	of	of	ADP
ajst-4909	114	3	astrophysics	astrophysic	NOUN
ajst-4909	114	4	&	&	CCONJ
ajst-4909	114	5	astronomy	astronomy	NOUN
ajst-4909	114	6	,	,	PUNCT
ajst-4909	114	7	2011	2011	NUM
ajst-4909	114	8	.	.	PUNCT
ajst-4909	115	1	[	[	X
ajst-4909	115	2	7	7	X
ajst-4909	115	3	]	]	X
ajst-4909	115	4	keenan	keenan	PROPN
ajst-4909	115	5	p	p	PROPN
ajst-4909	115	6	c.	c.	PROPN
ajst-4909	115	7	revised	revise	VERB
ajst-4909	115	8	mk	mk	PROPN
ajst-4909	115	9	spectral	spectral	ADJ
ajst-4909	115	10	classification	classification	NOUN
ajst-4909	115	11	of	of	ADP
ajst-4909	115	12	the	the	DET
ajst-4909	115	13	red	red	ADJ
ajst-4909	115	14	carbon	carbon	NOUN
ajst-4909	115	15	stars[j	stars[j	PROPN
ajst-4909	115	16	]	]	PUNCT
ajst-4909	115	17	.	.	PUNCT
ajst-4909	116	1	publications	publication	NOUN
ajst-4909	116	2	of	of	ADP
ajst-4909	116	3	the	the	DET
ajst-4909	116	4	astronomical	astronomical	ADJ
ajst-4909	116	5	society	society	NOUN
ajst-4909	116	6	of	of	ADP
ajst-4909	116	7	the	the	DET
ajst-4909	116	8	pacific	pacific	PROPN
ajst-4909	116	9	,	,	PUNCT
ajst-4909	116	10	1993	1993	NUM
ajst-4909	116	11	,	,	PUNCT
ajst-4909	116	12	105(691	105(691	NUM
ajst-4909	116	13	):	):	PUNCT
ajst-4909	116	14	905.y	905.y	NOUN
ajst-4909	116	15	.	.	PUNCT
ajst-4909	116	16	yorozu	yorozu	PROPN
ajst-4909	116	17	,	,	PUNCT
ajst-4909	116	18	m.	m.	PROPN
ajst-4909	116	19	hirano	hirano	PROPN
ajst-4909	116	20	,	,	PUNCT
ajst-4909	116	21	k.	k.	PROPN
ajst-4909	116	22	oka	oka	PROPN
ajst-4909	116	23	,	,	PUNCT
ajst-4909	116	24	and	and	CCONJ
ajst-4909	116	25	y.	y.	PROPN
ajst-4909	116	26	tagawa	tagawa	PROPN
ajst-4909	116	27	,	,	PUNCT
ajst-4909	116	28	“	"	PUNCT
ajst-4909	116	29	electron	electron	NOUN
ajst-4909	116	30	spectroscopy	spectroscopy	NOUN
ajst-4909	116	31	studies	study	NOUN
ajst-4909	116	32	on	on	ADP
ajst-4909	116	33	magnetooptical	magnetooptical	ADJ
ajst-4909	116	34	media	medium	NOUN
ajst-4909	116	35	and	and	CCONJ
ajst-4909	116	36	plastic	plastic	NOUN
ajst-4909	116	37	substrate	substrate	NOUN
ajst-4909	116	38	interfaces	interface	NOUN
ajst-4909	116	39	(	(	PUNCT
ajst-4909	116	40	translation	translation	NOUN
ajst-4909	116	41	journals	journal	NOUN
ajst-4909	116	42	style	style	NOUN
ajst-4909	116	43	)	)	PUNCT
ajst-4909	116	44	,	,	PUNCT
ajst-4909	116	45	”	"	PUNCT
ajst-4909	116	46	ieee	ieee	NOUN
ajst-4909	116	47	transl	transl	PROPN
ajst-4909	116	48	.	.	PUNCT
ajst-4909	117	1	j.	j.	PROPN
ajst-4909	117	2	magn	magn	PROPN
ajst-4909	117	3	.	.	PUNCT
ajst-4909	118	1	jpn	jpn	PROPN
ajst-4909	118	2	.	.	PROPN
ajst-4909	118	3	,	,	PUNCT
ajst-4909	118	4	vol	vol	NOUN
ajst-4909	118	5	.	.	PROPN
ajst-4909	118	6	2	2	NUM
ajst-4909	118	7	,	,	PUNCT
ajst-4909	118	8	aug	aug	PROPN
ajst-4909	118	9	.	.	PROPN
ajst-4909	118	10	1987	1987	NUM
ajst-4909	118	11	,	,	PUNCT
ajst-4909	118	12	pp	pp	ADP
ajst-4909	118	13	.	.	PUNCT
ajst-4909	119	1	740–741	740–741	NUM
ajst-4909	119	2	[	[	X
ajst-4909	119	3	dig	dig	X
ajst-4909	119	4	.	.	PUNCT
ajst-4909	120	1	9th	9th	ADJ
ajst-4909	120	2	annu	annu	PROPN
ajst-4909	120	3	.	.	PUNCT
ajst-4909	120	4	conf	conf	NOUN
ajst-4909	120	5	.	.	PUNCT
ajst-4909	121	1	magnetics	magnetic	NOUN
ajst-4909	121	2	japan	japan	PROPN
ajst-4909	121	3	,	,	PUNCT
ajst-4909	121	4	1982	1982	NUM
ajst-4909	121	5	,	,	PUNCT
ajst-4909	121	6	p.	p.	NOUN
ajst-4909	121	7	301	301	NUM
ajst-4909	121	8	]	]	PUNCT
ajst-4909	121	9	.	.	PUNCT
ajst-4909	122	1	[	[	X
ajst-4909	122	2	8	8	NUM
ajst-4909	122	3	]	]	X
ajst-4909	122	4	zhao	zhao	PROPN
ajst-4909	122	5	g	g	PROPN
ajst-4909	122	6	,	,	PUNCT
ajst-4909	122	7	zhao	zhao	PROPN
ajst-4909	122	8	y	y	PROPN
ajst-4909	122	9	h	h	PROPN
ajst-4909	122	10	,	,	PUNCT
ajst-4909	122	11	chu	chu	PROPN
ajst-4909	122	12	y	y	PROPN
ajst-4909	122	13	q	q	PROPN
ajst-4909	122	14	,	,	PUNCT
ajst-4909	122	15	et	et	PROPN
ajst-4909	122	16	al	al	PROPN
ajst-4909	122	17	.	.	PROPN
ajst-4909	123	1	lamost	lamost	PROPN
ajst-4909	123	2	spectral	spectral	ADJ
ajst-4909	123	3	survey	survey	NOUN
ajst-4909	123	4	—	—	PUNCT
ajst-4909	123	5	an	an	DET
ajst-4909	123	6	overview[j	overview[j	PROPN
ajst-4909	123	7	]	]	PUNCT
ajst-4909	123	8	.	.	PUNCT
ajst-4909	124	1	research	research	NOUN
ajst-4909	124	2	in	in	ADP
ajst-4909	124	3	astronomy	astronomy	NOUN
ajst-4909	124	4	and	and	CCONJ
ajst-4909	124	5	astrophysics	astrophysic	NOUN
ajst-4909	124	6	,	,	PUNCT
ajst-4909	124	7	2012	2012	NUM
ajst-4909	124	8	,	,	PUNCT
ajst-4909	124	9	12(7	12(7	NUM
ajst-4909	124	10	):	):	PUNCT
ajst-4909	124	11	723	723	NUM
ajst-4909	124	12	.	.	PUNCT
ajst-4909	125	1	[	[	X
ajst-4909	125	2	9	9	NUM
ajst-4909	125	3	]	]	X
ajst-4909	125	4	sharma	sharma	PROPN
ajst-4909	125	5	m	m	PROPN
ajst-4909	125	6	p	p	NOUN
ajst-4909	125	7	,	,	PUNCT
ajst-4909	125	8	saxena	saxena	PROPN
ajst-4909	125	9	r	r	PROPN
ajst-4909	125	10	p	p	PROPN
ajst-4909	125	11	.	.	PUNCT
ajst-4909	126	1	international	international	ADJ
ajst-4909	126	2	journal	journal	PROPN
ajst-4909	126	3	on	on	ADP
ajst-4909	126	4	recent	recent	ADJ
ajst-4909	126	5	and	and	CCONJ
ajst-4909	126	6	innovation	innovation	NOUN
ajst-4909	126	7	trends	trend	NOUN
ajst-4909	126	8	in	in	ADP
ajst-4909	126	9	computing	computing	NOUN
ajst-4909	126	10	and	and	CCONJ
ajst-4909	126	11	communication	communication	NOUN
ajst-4909	126	12	a	a	DET
ajst-4909	126	13	review	review	NOUN
ajst-4909	126	14	on	on	ADP
ajst-4909	126	15	non	non	ADJ
ajst-4909	126	16	linear	linear	ADJ
ajst-4909	126	17	dimensionality	dimensionality	NOUN
ajst-4909	126	18	reduction	reduction	NOUN
ajst-4909	126	19	techniques	technique	NOUN
ajst-4909	126	20	for	for	ADP
ajst-4909	126	21	face	face	NOUN
ajst-4909	126	22	recognition[j	recognition[j	NOUN
ajst-4909	126	23	]	]	PUNCT
ajst-4909	126	24	.	.	PUNCT
ajst-4909	127	1	2019	2019	NUM
ajst-4909	127	2	.	.	PUNCT
ajst-4909	128	1	[	[	X
ajst-4909	128	2	10	10	NUM
ajst-4909	128	3	]	]	PUNCT
ajst-4909	128	4	park	park	NOUN
ajst-4909	128	5	c	c	PROPN
ajst-4909	128	6	h	h	PROPN
ajst-4909	128	7	,	,	PUNCT
ajst-4909	128	8	park	park	NOUN
ajst-4909	128	9	h	h	NOUN
ajst-4909	128	10	.	.	PUNCT
ajst-4909	129	1	a	a	DET
ajst-4909	129	2	comparison	comparison	NOUN
ajst-4909	129	3	of	of	ADP
ajst-4909	129	4	generalized	generalized	ADJ
ajst-4909	129	5	linear	linear	ADJ
ajst-4909	129	6	discriminant	discriminant	NOUN
ajst-4909	129	7	analysis	analysis	NOUN
ajst-4909	129	8	algorithms[j	algorithms[j	PROPN
ajst-4909	129	9	]	]	X
ajst-4909	129	10	.	.	PUNCT
ajst-4909	130	1	pattern	pattern	NOUN
ajst-4909	130	2	recognition	recognition	NOUN
ajst-4909	130	3	,	,	PUNCT
ajst-4909	130	4	2008	2008	NUM
ajst-4909	130	5	,	,	PUNCT
ajst-4909	130	6	41(3):1083	41(3):1083	PROPN
ajst-4909	130	7	-	-	NOUN
ajst-4909	130	8	1097	1097	NUM
ajst-4909	130	9	.	.	PUNCT
ajst-4909	131	1	[	[	X
ajst-4909	131	2	11	11	NUM
ajst-4909	131	3	]	]	PUNCT
ajst-4909	131	4	mccallum	mccallum	PROPN
ajst-4909	131	5	a	a	PRON
ajst-4909	131	6	,	,	PUNCT
ajst-4909	131	7	roweis	roweis	PROPN
ajst-4909	131	8	s	s	PART
ajst-4909	131	9	.	.	PUNCT
ajst-4909	132	1	proceedings	proceeding	NOUN
ajst-4909	132	2	,	,	PUNCT
ajst-4909	132	3	twenty	twenty	NUM
ajst-4909	132	4	-	-	PUNCT
ajst-4909	132	5	fifth	fifth	ADJ
ajst-4909	132	6	international	international	ADJ
ajst-4909	132	7	conference	conference	NOUN
ajst-4909	132	8	on	on	ADP
ajst-4909	132	9	machine	machine	NOUN
ajst-4909	132	10	learning	learning	NOUN
ajst-4909	132	11	:	:	PUNCT
ajst-4909	132	12	preface	preface	NOUN
ajst-4909	132	13	.	.	PUNCT
ajst-4909	133	1	2008	2008	NUM
ajst-4909	133	2	.	.	PUNCT
ajst-4909	134	1	[	[	X
ajst-4909	134	2	12	12	NUM
ajst-4909	134	3	]	]	PUNCT
ajst-4909	134	4	gisbrecht	gisbrecht	NOUN
ajst-4909	134	5	,	,	PUNCT
ajst-4909	134	6	mokbel	mokbel	NOUN
ajst-4909	134	7	,	,	PUNCT
ajst-4909	134	8	hammer	hammer	NOUN
ajst-4909	134	9	.	.	PUNCT
ajst-4909	135	1	linear	linear	ADJ
ajst-4909	135	2	basis	basis	NOUN
ajst-4909	135	3	-	-	PUNCT
ajst-4909	135	4	function	function	NOUN
ajst-4909	135	5	t	t	PROPN
ajst-4909	135	6	-	-	PUNCT
ajst-4909	135	7	sne	sne	PROPN
ajst-4909	135	8	for	for	ADP
ajst-4909	135	9	fast	fast	ADJ
ajst-4909	135	10	nonlinear	nonlinear	ADJ
ajst-4909	135	11	dimensionality	dimensionality	NOUN
ajst-4909	135	12	reduction[c]//	reduction[c]//	PROPN
ajst-4909	135	13	international	international	ADJ
ajst-4909	135	14	joint	joint	ADJ
ajst-4909	135	15	conference	conference	NOUN
ajst-4909	135	16	on	on	ADP
ajst-4909	135	17	neural	neural	ADJ
ajst-4909	135	18	networks	network	NOUN
ajst-4909	135	19	.	.	PUNCT
ajst-4909	136	1	ieee	ieee	NOUN
ajst-4909	136	2	,	,	PUNCT
ajst-4909	136	3	2012	2012	NUM
ajst-4909	136	4	.	.	PUNCT